c© 2002 Association for Computational Linguistics
Summarizing Scientific Articles:
Experiments with Relevance and
Rhetorical Status
Simone Teufel
∗
Marc Moens
†
Cambridge University Rhetorical Systems and University of
Edinburgh
In this article we propose a strategy for the summarization of scientific articles that concentrates
on the rhetorical status of statements in an article: Material for summaries is selected in such a
way that summaries can highlight the new contribution of the source article and situate it with
respect to earlier work.
We provide a gold standard for summaries of this kind consisting of a substantial corpus of
conference articles in computational linguistics annotated with human judgments of the rhetorical
status and relevance of each sentence in the articles. We present several experiments measuring
our judges’ agreement on these annotations.
We also present an algorithm that, on the basis of the annotated training material, selects
content from unseen articles and classifies it into a fixed set of seven rhetorical categories. The
output of this extraction and classification system can be viewed as a single-document summary
in its own right; alternatively, it provides starting material for the generation of task-oriented
and user-tailored summaries designed to give users an overview of a scientific field.
1. Introduction
Summarization systems are often two-phased, consisting of a content selection step
followed by a regeneration step. In the first step, text fragments (sentences or clauses)
are assigned a score that reflects how important or contentful they are. The highest-
ranking material can then be extracted and displayed verbatim as “extracts” (Luhn
1958; Edmundson 1969; Paice 1990; Kupiec, Pedersen, and Chen 1995). Extracts are
often useful in an information retrieval environment since they give users an idea as
to what the source document is about (Tombros and Sanderson 1998; Mani et al. 1999),
but they are texts of relatively low quality. Because of this, it is generally accepted that
some kind of postprocessing should be performed to improve the final result, by
shortening, fusing, or otherwise revising the material (Grefenstette 1998; Mani, Gates,
and Bloedorn 1999; Jing and McKeown 2000; Barzilay et al. 2000; Knight and Marcu
2000).
The extent to which it is possible to do postprocessing is limited, however, by
the fact that contentful material is extracted without information about the general
discourse context in which the material occurred in the source text. For instance, a
sentence describing the solution to a scientific problem might give the main contri-
∗ Simone Teufel, Computer Laboratory, Cambridge University, JJ Thomson Avenue, Cambridge,
CB3 OFD, England. E-mail: Simone.Teufel@cl.cam.ac.uk
† Marc Moens, Rhetorical Systems and University of Edinburgh, 2 Buccleuch Place, Edinburgh, EH8 9LS,
Scotland. E-mail: marc@cogsci.ed.ac.uk
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Computational Linguistics Volume 28, Number 4
bution of the paper, but it might also refer to a previous approach that the authors
criticize. Depending on its rhetorical context, the same sentence should be treated very
differently in a summary. We propose in this article a method for sentence and con-
tent selection from source texts that adds context in the form of information about the
rhetorical role the extracted material plays in the source text. This added contextual
information can then be used to make the end product more informative and more
valuable than sentence extracts.
Our application domain is the summarization of scientific articles. Summariza-
tion of such texts requires a different approach from, for example, that used in the
summarization of news articles. For example, Barzilay, McKeown, and Elhadad (1999)
introduce the concept of information fusion, which is based on the identification of re-
current descriptions of the same events in news articles. This approach works well
because in the news domain, newsworthy events are frequently repeated over a short
period of time. In scientific writing, however, similar “events” are rare: The main focus
is on new scientific ideas, whose main characteristic is their uniqueness and difference
from previous ideas.
Other approaches to the summarization of news articles make use of the typical
journalistic writing style, for example, the fact that the most newsworthy information
comes first; as a result, the first few sentences of a news article are good candidates
for a summary (Brandow, Mitze, and Rau 1995; Lin and Hovy 1997). The structure
of scientific articles does not reflect relevance this explicitly. Instead, the introduction
often starts with general statements about the importance of the topic and its history
in the field; the actual contribution of the paper itself is often given much later.
The length of scientific articles presents another problem. Let us assume that our
overall summarization strategy is first to select relevant sentences or concepts, and
then to synthesize summaries using this material. For a typical 10- to 20-sentence
news wire story, a compression to 20% or 30% of the source provides a reasonable
input set for the second step. The extracted sentences are still thematically connected,
and concepts in the sentences are not taken completely out of context. In scientific ar-
ticles, however, the compression rates have to be much higher: Shortening a 20-page
journal article to a half-page summary requires a compression to 2.5% of the original.
Here, the problematic fact that sentence selection is context insensitive does make a
qualitative difference. If only one sentence per two pages is selected, all information
about how the extracted sentences and their concepts relate to each other is lost; with-
out additional information, it is difficult to use the selected sentences as input to the
second stage.
We present an approach to summarizing scientific articles that is based on the idea
of restoring the discourse context of extracted material by adding the rhetorical status
to each sentence in a document. The innovation of our approach is that it defines
principles for content selection specifically for scientific articles and that it combines
sentence extraction with robust discourse analysis. The output of our system is a list
of extracted sentences along with their rhetorical status (e.g. sentence 11 describes the
scientific goal of the paper, and sentence 9 criticizes previous work), as illustrated in
Figure 1. (The example paper we use throughout the article is F. Pereira, N. Tishby, and
L. Lee’s “Distributional Clustering of English Words” [ACL-1993, cmp lg/9408011]; it
was chosen because it is the paper most often cited within our collection.) Such lists
serve two purposes: in themselves, they already provide a better characterization of
scientific articles than sentence extracts do, and in the longer run, they will serve as
better input material for further processing.
An extrinsic evaluation (Teufel 2001) shows that the output of our system is al-
ready a useful document surrogate in its own right. But postprocessing could turn
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Teufel and Moens Summarizing Scientific Articles
AIM 10 Our research addresses some of the same questions and uses similar raw data,
but we investigate how to factor word association tendencies into associations
of words to certain hidden senses classes and associations between the classes
themselves.
11 While it may be worthwhile to base such a model on preexisting sense classes
(Resnik, 1992), in the work described here we look at how to derive the classes
directly from distributional data.
162 We have demonstrated that a general divisive clustering procedure for probability
distributions can be used to group words according to their participation in
particular grammatical relations with other words.
BASIS 19 The corpus used in our first experiment was derived from newswire text auto-
matically parsed by Hindle’s parser Fidditch (Hindle, 1993).
113 The analogy with statistical mechanics suggests a deterministic annealing pro-
cedure for clustering (Rose et al., 1990), in which the number of clusters is
determined through a sequence of phase transitions by continuously increasing
the parameter EQN following an annealing schedule.
CONTRAST 9 His notion of similarity seems to agree with our intuitions in many cases, but it is
not clear how it can be used directly to construct word classes and corresponding
models of association.
14 Class construction is then combinatorially very demanding and depends on
frequency counts for joint events involving particular words, a potentially un-
reliable source of information as we noted above.
Figure 1
Extract of system output for example paper.
0 This paper’s topic is to automatically classify words according to their contexts of use.
4 The problem is that for large enough corpora the number of possible joint events is much
larger than the number of event occurrences in the corpus, so many events are seen rarely
or never, making their frequency counts unreliable estimates of their probabilities. 162
This paper’s specific goal is to group words according to their participation in particular
grammatical relations with other words, 22 more specifically to classify nouns according
to their distribution as direct objects of verbs.
Figure 2
Nonexpert summary, general purpose.
the rhetorical extracts into something even more valuable: The added rhetorical con-
text allows for the creation of a new kind of summary. Consider, for instance, the
user-oriented and task-tailored summaries shown in Figures 2 and 3. Their composi-
tion was guided by fixed building plans for different tasks and different user models,
whereby the building blocks are defined as sentences of a specific rhetorical status.
In our example, most textual material is extracted verbatim (additional material is
underlined in Figures 2 and 3; the original sentences are given in Figure 5). The first
example is a short abstract generated for a nonexpert user and for general information;
its first two sentences give background information about the problem tackled. The
second abstract is aimed at an expert; therefore, no background is given, and instead
differences between this approach and similar ones are described.
The actual construction of these summaries is a complex process involving tasks
such as sentence planning, lexical choice and syntactic realization, tasks that are outside
the scope of this article. The important point is that it is the knowledge about the
rhetorical status of the sentences that enables the tailoring of the summaries according
to users’ expertise and task. The rhetorical status allows for other kinds of applications
too: Several articles can be summarized together, contrasts or complementarity among
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Computational Linguistics Volume 28, Number 4
44 This paper’s goal is to organise a set of linguistic objects such as words according to
the contexts in which they occur, for instance grammatical constructions or n-grams.
22 More specifically: the goal is to classify nouns according to their distribution as direct
objects of verbs. 5 Unlike Hindle (1990), 9 this approach constructs word classes and
corresponding models of association directly. 14 In comparison to Brown et al. (1992),
the method is combinatorially less demanding and does not depend on frequency counts
for joint events involving particular words, a potentially unreliable source of information.
Figure 3
Expert summary, contrastive links.
articles can be expressed, and summaries can be displayed together with citation links
to help users navigate several related papers.
The rest of this article is structured as follows: section 2 describes the theoretical
and empirical aspects of document structure we model in this article. These aspects
include rhetorical status and relatedness:
• Rhetorical status in terms of problem solving: What is the goal and
contribution of the paper? This type of information is often marked by
metadiscourse and by conventional patterns of presentation (cf.
section 2.1).
• Rhetorical status in terms of intellectual attribution: What information is
claimed to be new, and which statements describe other work? This type
of information can be recognized by following the “agent structure” of
text, that is, by looking at all grammatical subjects occurring in sequence
(cf. section 2.2).
• Relatedness among articles: What articles is this work similar to, and in
what respect? This type of information can be found by examining fixed
indicator phrases like in contrast to ..., section headers, and citations (cf.
section 2.3).
These aspects of rhetorical status are encoded in an annotation scheme that we present
in section 2.4. Annotation of relevance is covered in section 2.5.
In section 3, we report on the construction of a gold standard for rhetorical status
and relevance and on the measurement of agreement among human annotators. We
then describe in section 4 our system that simulates the human annotation. Section 5
presents an overview of the intrinsic evaluation we performed, and section 6 closes
with a summary of the contribution of this work, its limitations, and suggestions for
future work.
2. Rhetorical Status, Citations, and Relevance
It is important for our task to find the right definition of rhetorical status to describe the
content in scientific articles. The definition should both capture generalizations about
the nature of scientific texts and also provide the right kind of information to enable the
construction of better summaries for a practical application. Another requirement is
that the analysis should be applicable to research articles from different presentational
traditions and subject matters.
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Teufel and Moens Summarizing Scientific Articles
For the development of our scheme, we used the chronologically first 80 articles
in our corpus of conference articles in computational linguistics (articles presented
at COLING, ANLP, and (E)ACL conferences or workshops). Because of the inter-
disciplinarity of the field, the papers in this collection cover a challenging range of
subject matters, such as logic programming, statistical language modeling, theoreti-
cal semantics, computational dialectology, and computational psycholinguistics. Also,
the research methodology and tradition of presentation is very different among these
fields; (computer scientists write very different papers than theoretical linguists). We
thus expect our analysis to be equally applicable in a wider range of disciplines and
subdisciplines other than those named.
2.1 Rhetorical Status
Our model relies on the following dimensions of document structure in scientific
articles.
Problem structure. Research is often described as a problem-solving activity (Jordan
1984; Trawinski 1989; Zappen 1983). Three information types can be expected to occur
in any research article: problems (research goals), solutions (methods), and results. In
many disciplines, particularly the experimental sciences, this problem-solution struc-
ture has been crystallized in a fixed presentation of the scientific material as introduc-
tion, method, result and discussion (van Dijk 1980). But many texts in computational
linguistics do not adhere to this presentation, and our analysis therefore has to be
based on the underlying logical (rhetorical) organization, using textual representation
only as an indication.
Intellectual attribution. Scientific texts should make clear what the new contribution
is, as opposed to previous work (specific other researchers’ approaches) and back-
ground material (generally accepted statements). We noticed that intellectual attribu-
tion has a segmental character. Statements in a segment without any explicit attribution
are often interpreted as belonging to the most recent explicit attribution statement
(e.g., Other researchers claim that). Our rhetorical scheme assumes that readers have
no difficulty in understanding intellectual attribution, an assumption that we verified
experimentally.
Scientific argumentation. In contrast to the view of science as a disinterested “fact
factory,” researchers like Swales (1990) have long claimed that there is a strong social
aspect to science, because the success of a researcher is correlated with her ability to
convince the field of the quality of her work and the validity of her arguments. Au-
thors construct an argument that Myers (1992) calls the “rhetorical act of the paper”:
The statement that their work is a valid contribution to science. Swales breaks down
this “rhetorical act” into single, nonhierarchical argumentative moves (i.e., rhetorically
coherent pieces of text, which perform the same communicative function). His Con-
structing a Research Space (CARS) model shows how patterns of these moves can be
used to describe the rhetorical structure of introduction sections of physics articles.
Importantly, Swales’s moves describe the rhetorical status of a text segment with re-
spect to the overall message of the document, and not with respect to adjacent text
segments.
Attitude toward other people’s work. We are interested in how authors include refer-
ence to other work into their argument. In the flow of the argument, each piece of
other work is mentioned for a specific reason: it is portrayed as a rival approach, as
a prior approach with a fault, or as an approach contributing parts of the authors’
own solution. In well-written papers, this relation is often expressed in an explicit
way. The next section looks at the stylistic means available to the author to express
the connection between previous approaches and their own work.
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2.2 Metadiscourse and Agentivity
Explicit metadiscourse is an integral aspect of scientific argumentation and a way of
expressing attitude toward previous work. Examples for metadiscourse are phrases
like we argue that and in contrast to common belief, we. Metadiscourse is ubiquitous in
scientific writing: Hyland (1998) found a metadiscourse phrase on average after every
15 words in running text.
A large proportion of scientific metadiscourse is conventionalized, particularly in
the experimental sciences, and particularly in the methodology or result section (e.g.,
we present original work ...,orAn ANOVA analysis revealed a marginal interaction/a main ef-
fect of ...). Swales (1990) lists many such fixed phrases as co-occurring with the moves
of his CARS model (pages 144, 154–158, 160–161). They are useful indicators of overall
importance (Pollock and Zamora 1975); they can also be relatively easily recognized
with information extraction techniques (e.g., regular expressions). Paice (1990) intro-
duces grammars for pattern matching of indicator phrases, e.g., the aim/purpose of this
paper/article/study and we conclude/propose.
Apart from this conventionalized metadiscourse, we noticed that our corpus con-
tains a large number of metadiscourse statements that are less formalized: statements
about aspects of the problem-solving process or the relation to other work. Figure 4,
for instance, shows that there are many ways to say that one’s research is based on
somebody else’s (“research continuation”). The sentences do not look similar on the
surface: The syntactic subject can be the authors, the originators of the method, or
even the method itself. Also, the verbs are very different (base, be related, use, follow).
Some sentences use metaphors of change and creation. The wide range of linguistic
expression we observed presents a challenge for recognition and correct classification
using standard information extraction patterns.
With respect to agents occurring in scientific metadiscourse, we make two sug-
gestions: (1) that scientific argumentation follows prototypical patterns and employs
recurrent types of agents and actions and (2) that it is possible to recognize many of
these automatically. Agents play fixed roles in the argumentation, and there are so
• We employ Suzuki’s algorithm to learn case frame patterns as dendroid distributions.
(9605013)
• Our method combines similarity-based estimates with Katz’s back-off scheme, which is widely used for language
modeling in speech recognition. (9405001)
• Thus, we base our model on the work of Clark and Wilkes-Gibbs (1986), and Heeman and Hirst (1992) ...
(9405013)
• The starting point for this work was Scha and Polanyi’s discourse grammar (Scha and Polanyi, 1988; Pruest et
al., 1994). (9502018)
• We use the framework for the allocation and transfer of control of Whittaker and Stenton (1988).
(9504007)
• Following Laur (1993), we consider simple prepositions (like “in”) as well as prepositional phrases (like “in front
of”). (9503007)
• Our lexicon is based on a finite-state transducer lexicon (Karttunen et al., 1992).
(9503004)
• Instead of ...we will adopt a simpler, monostratal representation that is more closely related to those found in
dependency grammars (e.g., Hudson (1984)). (9408014)
Figure 4
Statements expressing research continuation, with source article number.
415
Teufel and Moens Summarizing Scientific Articles
few of these roles that they can be enumerated: agents appear as rivals, as contrib-
utors of part of the solution (they), as the entire research community in the field, or
as the authors of the paper themselves (we). Note the similarity of agent roles to the
three kinds of intellectual attribution mentioned above. We also propose prototypical
actions frequently occurring in scientific discourse: the field might agree, a particular
researcher can suggest something, and a certain solution could either fail or be success-
ful. In section 4 we will describe the three features used in our implementation that
recognize metadiscourse.
Another important construct that expresses relations to other researchers’ work is
formal citations, to which we will now turn.
2.3 Citations and Relatedness
Citation indexes are constructs that contain pointers between cited texts and citing
texts (Garfield 1979), traditionally in printed form. When done on-line (as in CiteSeer
[Lawrence, Giles, and Bollacker 1999], or as in Nanba and Okumura’s [1999] work),
citations are presented in context for users to browse. Browsing each citation is time-
consuming, but useful: just knowing that an article cites another is often not enough.
One needs to read the context of the citation to understand the relation between the
articles. Citations may vary in many dimensions; for example, they can be central or
perfunctory, positive or negative (i.e., critical); apart from scientific reasons, there is
also a host of social reasons for citing (“politeness, tradition, piety” [Ziman 1969]).
We concentrate on two citation contexts that are particularly important for the
information needs of researchers:
• Contexts in which an article is cited negatively or contrastively.
• Contexts in which an article is cited positively or in which the authors
state that their own work originates from the cited work.
A distinction among these contexts would enable us to build more informative citation
indexes. We suggest that such a rhetorical distinction can be made manually and
automatically for each citation; we use a large corpus of scientific papers along with
humans’ judgments of this distinction to train a system to make such distinctions.
2.4 The Rhetorical Annotation Scheme
Our rhetorical annotation scheme (cf. Table 1) encodes the aspects of scientific argu-
mentation, metadiscourse, and relatedness to other work described before. The cat-
egories are assigned to full sentences, but a similar scheme could be developed for
clauses or phrases.
The annotation scheme is nonoverlapping and nonhierarchical, and each sentence
must be assigned to exactly one category. As adjacent sentences of the same status can
be considered to form zones of the same rhetorical status, we call the units rhetorical
zones. The shortest of these zones are one sentence long.
The rhetorical status of a sentence is determined on the basis of the global context
of the paper. For instance, whereas the OTHER category describes all neutral descrip-
tions of other researchers’ work, the categories BASIS and CONTRAST are applicable to
sentences expressing a research continuation relationship or a contrast to other work.
Generally accepted knowledge is classified as BACKGROUND, whereas the author’s own
work is separated into the specific research goal (AIM) and all other statements about
the author’s own work (OWN).
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Table 1
Annotation scheme for rhetorical status.
AIM Specific research goal of the current paper
TEXTUAL Statements about section structure
OWN (Neutral) description of own work presented in current paper: Method-
ology, results, discussion
BACKGROUND Generally accepted scientific background
CONTRAST Statements of comparison with or contrast to other work; weaknesses of
other work
BASIS Statements of agreement with other work or continuation of other work
OTHER (Neutral) description of other researchers’ work
The annotation scheme expresses important discourse and argumentation aspects
of scientific articles, but with its seven categories it is not designed to model the full
complexity of scientific texts. The category OWN, for instance, could be further sub-
divided into method (solution), results, and further work, which is not done in the
work reported here. There is a conflict between explanatory power and the simplicity
necessary for reliable human and automatic classification, and we decided to restrict
ourselves to the rhetorical distinctions that are most salient and potentially most useful
for several information access applications. The user-tailored summaries and more in-
formative citation indexes we mentioned before are just two such applications; another
one is the indexing and previewing of the internal structure of the article. To make
such indexing and previewing possible, our scheme contains the additional category
TEXTUAL, which captures previews of section structure (section 2 describes our data ...).
Such previews would make it possible to label sections with the author’s indication
of their contents.
Our rhetorical analysis, as noted above, is nonhierarchical, in contrast to Rhetorical
Structure Theory (RST) (Mann and Thompson 1987; Marcu 1999), and it concerns
text pieces at a lower level of granularity. Although we do agree with RST that the
structure of text is hierarchical in many cases, it is our belief that the relevance and
function of certain text pieces can be determined without analyzing the full hierarchical
structure of the text. Another difference between our analysis and that of RST is that
our analysis aims at capturing the rhetorical status of a piece of text in respect to the
overall message, and not in relation to adjacent pieces of text.
2.5 Relevance
As our immediate goal is to select important content from a text, we also need a second
set of gold standards that are defined by relevance (as opposed to rhetorical status).
Relevance is a difficult issue because it is situational to a unique occasion (Saracevic
1975; Sparck Jones 1990; Mizzaro 1997): Humans perceive relevance differently from
each other and differently in different situations. Paice and Jones (1993) report that they
abandoned an informal sentence selection experiment in which they used agriculture
articles and experts in the field as participants, as the participants were too strongly
influenced by their personal research interest.
As a result of subjectivity, a number of human sentence extraction experiments
over the years have resulted in low agreement figures. Rath, Resnick, and Savage
(1961) report that six participants agreed on only 8% of 20 sentences they were asked
to select out of short Scientific American texts and that five agreed on 32% of the
sentences. They found that after six weeks, subjects selected on average only 55%
of the sentences they themselves selected previously. Edmundson and Wyllys (1961)
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Teufel and Moens Summarizing Scientific Articles
find similarly low human agreement for research articles. More recent experiments
reporting more positive results all used news text (Jing et al. 1998; Zechner 1995).
As discussed above, the compression rates on news texts are far lower: there are
fewer sentences from which to choose, making it easier to agree on which ones to
select. Sentence selection from scientific texts also requires more background knowl-
edge, thus importing an even higher level of subjectivity into sentence selection
experiments.
Recently, researchers have been looking for more objective definitions of relevance.
Kupiec, Pedersen, and Chen (1995) define relevance by abstract similarity: A sentence
in a document is considered relevant if it shows a high level of similarity to a sentence
in the abstract. This definition of relevance has the advantage that it is fixed (i.e., the
researchers have no influence over it). It relies, however, on two assumptions: that the
writing style is such that there is a high degree of overlap between sentences in the
abstract and in the main text and that the abstract is indeed the target output that is
most adequate for the final task.
In our case, neither assumption holds. First, the experiments in Teufel and Moens
(1997) showed that in our corpus only 45% of the abstract sentences appear elsewhere
in the body of the document (either as a close variant or in identical form), whereas
Kupiec, Pedersen, and Chen report a figure of 79%. We believe that the reason for the
difference is that in our case the abstracts were produced by the document authors and
by professional abstractors in Kupiec, Pedersen, and Chen’s case. Author summaries
tend to be less systematic (Rowley 1982) and more “deep generated,” whereas sum-
maries by professional abstractors follow an internalized building plan (Liddy 1991)
and are often created through sentence extraction (Lancaster 1998).
Second, and more importantly, the abstracts and improved citation indexes we
intend to generate are not modeled on traditional summaries, which do not pro-
vide the type of information needed for the applications we have in mind. Infor-
mation about related work plays an important role in our strategy for summarization
and citation indexing, but such information is rarely found in abstracts. We empir-
ically found that the rhetorical status of information occurring in author abstracts
is very limited and consists mostly of information about the goal of the paper and
specifics of the solution. Details of the analysis we conducted on this topic are given in
section 3.2.2.
We thus decided to augment our corpus with an independent set of human judg-
ments of relevance. We wanted to replace the vague definition of relevance often
used in sentence extraction experiments with a more operational definition based on
rhetorical status. For instance, a sentence is considered relevant only if it describes
the research goal or states a difference with a rival approach. More details of the
instructions we used to make the relevance decisions are given in section 3.
Thus, we have two parallel human annotations in our corpus: rhetorical annotation
and relevance selection. In both tasks, each sentence in the articles is classified: Each
sentence receives one rhetorical category and also the label irrelevant or relevant. This
strategy can create redundant material (e.g., when the same fact is expressed in a
sentence in the introduction, a sentence in the conclusion, and one in the middle of
the document). But this redundancy also helps mitigate one of the main problems
with sentence-based gold standards, namely, the fact that there is no one single best
extract for a document. In our annotation, all qualifying sentences in the document
are identified and classified into the same group, which makes later comparisons
with system performance fairer. Also, later steps cannot only find redundancy in the
intermediate result and remove it, but also use the redundancy as an indication of
importance.
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Computational Linguistics Volume 28, Number 4
Aim:
10 Our research addresses some of the same questions and uses similar raw data, but we investigate
how to factor word association tendencies into associations of words to certain hidden senses
classes and associations between the classes themselves.
22 We will consider here only the problem of classifying nouns according to their distribution as
direct objects of verbs; the converse problem is formally similar.
25 The problem we study is how to use the EQN to classify the EQN.
44 In general, we are interested on how to organise a set of linguistic objects such as words according
to the contexts in which they occur, for instance grammatical constructions or n-grams.
46 Our problem can be seen as that of learning a joint distribution of pairs from a large sample of
pairs.
162 We have demonstrated that a general divisive clustering procedure for probability distributions
can be used to group words according to their participation in particular grammatical relations
with other words.
Background:
0 Methods for automatically classifying words according to their contexts of use have both scientific
and practical interest.
4 The problem is that for large enough corpora the number of possible joint events is much larger
than the number of event occurrences in the corpus, so many events are seen rarely or never,
making their frequency counts unreliable estimates of their probabilities.
Own (Details of Solution):
66 The first stage of an iteration is a maximum likelihood, or minimum distortion, estimation of
the cluster centroids given fixed membership probabilities.
140 The evaluation described below was performed on the largest data set we have worked with so far,
extracted from 44 million words of 1988 Associated Press newswire with the pattern matching
techniques mentioned earlier.
163 The resulting clusters are intuitively informative, and can be used to construct class-based word
coocurrence [sic] models with substantial predictive power.
Contrast with Other Approaches/Weaknesses of Other Approaches:
9 His notion of similarity seems to agree with our intuitions in many cases, but it is not clear how
it can be used directly to construct word classes and corresponding models of association.
14 Class construction is then combinatorially very demanding and depends on frequency counts
for joint events involving particular words, a potentially unreliable source of information as we
noted above.
41 However, this is not very satisfactory because one of the goals of our work is precisely to avoid
the problems of data sparseness by grouping words into classes.
Basis (Imported Solutions):
65 The combined entropy maximization entropy [sic] and distortion minimization is carried out
by a two-stage iterative process similar to the EM method (Dempster et al., 1977).
113 The analogy with statistical mechanics suggests a deterministic annealing procedure for clus-
tering (Rose et al., 1990), in which the number of clusters is determined through a sequence
of phase transitions by continuously increasing the parameter EQN following an annealing
schedule.
153 The data for this test was built from the training data for the previous one in the following way,
based on a suggestion by Dagan et al. (1993).
Figure 5
Example of manual annotation: Relevant sentences with rhetorical status.
Figure 5 gives an example of the manual annotation. Relevant sentences of all
rhetorical categories are shown. Our system creates a list like the one in Figure 5
automatically (Figure 12 shows the actual output of the system when run on the
example paper). In the next section, we turn to the manual annotation step and the
development of the gold standard used during system training and system evaluation.
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Teufel and Moens Summarizing Scientific Articles
3. Human Judgments: The Gold Standard
For any linguistic analysis that requires subjective interpretation and that is therefore
not objectively true or false, it is important to show that humans share some intuitions
about the analysis. This is typically done by showing that they can apply it indepen-
dently of each other and that the variation they display is bounded (i.e., not arbitrarily
high). The argument is strengthened if the judges are people other than the developers
of the analysis, preferably “na¨ıve” subjects (i.e., not computational linguists). Apart
from the cognitive validation of our analysis, high agreement is essential if the anno-
tated corpus is to be used as training material for a machine learning process, like the
one we describe in section 4. Noisy and unreliably annotated training material will
very likely deteriorate the classification performance.
In inherently subjective tasks, it is also common practice to consider human perfor-
mance as an upper bound. The theoretically best performance of a system is reached
if agreement among a pool of human annotators does not decrease when the system
is added to the pool. This is so because an automatic process cannot do any better in
this situation than to be indistinguishable from human performance.
3.1 Corpus
The annotated development corpus consists of 80 conference articles in computational
linguistics (12,188 sentences; 285,934 words). It is part of a larger corpus of 260 articles
(1.1 million words) that we collected from the CMP LG archive (CMP LG 1994). The
appendix lists the 80 articles (archive numbers, titles and authors) of our development
corpus; it consists of the 80 chronologically oldest articles in the larger corpus, con-
taining articles deposited between May 1994 and May 1996 (whereas the entire corpus
stretches until 2001).
Papers were included if they were presented at one of the following conferences
(or associated workshops): the annual meeting of the Association for Computational
Linguistics (ACL), the meeting of the European Chapter of the Association for Compu-
tational Linguistics (EACL), the conference on Applied Natural Language Processing
(ANLP), the International Joint Conference on Artificial Intelligence (IJCAI), or the In-
ternational Conference on Computational Linguistics (COLING). As mentioned above,
a wide range of different subdomains of the field of computational linguistics are
covered.
We added Extensible Markup Language (XML) markup to the corpus: Titles, au-
thors, conference, date, abstract, sections, headlines, paragraphs, and sentences were
marked up. Equations, tables, images were removed and replaced by placeholders.
Bibliography lists were marked up and parsed. Citations and occurrences of au-
thor names in running text were recognized, and self-citations were recognized and
specifically marked up. (Linguistic) example sentences and example pseudocode were
manually marked up, such that clean textual material (i.e., the running text of the
article without interruptions) was isolated for automatic processing. The implemen-
tation uses the Text Tokenization Toolkit (TTT) software (Grover, Mikheev, and
Matheson 1999).
3.2 Annotation of Rhetorical Status
The annotation experiment described here (and in Teufel, Carletta, and Moens [1999]
in more detail) tests the rhetorical annotation scheme presented in section 2.4.
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Computational Linguistics Volume 28, Number 4
3.2.1 Rationale and Experimental Design.
Annotators. Three task-trained annotators were used: Annotators A and B have
degrees in cognitive science and speech therapy. They were paid for the experiment.
Both are well-used to reading scientific articles for their studies and roughly under-
stand the contents of the articles they annotated because of the closeness of their fields
to computational linguistics. Annotator C is the first author. We did not want to de-
clare annotator C the expert annotator; we believe that in subjective tasks like the one
described here, there are no real experts.
Guidelines. Written guidelines (17 pages) describe the semantics of the categories,
ambiguous cases, and decision strategies. The guidelines also include the decision tree
reproduced in Figure 6.
Training. Annotators received a total of 20 hours of training. Training consisted
of the presentation of annotation of six example papers and the annotation of eight
training articles under real conditions (i.e., independently). In subsequent training ses-
sions, decision criteria for difficult cases encountered in the training articles were dis-
cussed. Obviously, the training articles were excluded from measurements of human
agreement.
Materials and procedure. Twenty-five articles were used for annotation. As no annota-
tion tool was available at the time, annotation was performed on paper; the categories
were later transferred to the electronic versions of the articles by hand. Skim-reading
and annotation typically took between 20 and 30 minutes per article, but there were
no time restrictions. No communication between the annotators was allowed during
annotation. Six weeks after the initial annotation, annotators were asked to reannotate
6 random articles out of the 25.
Evaluation measures. We measured two formal properties of the annotation: stability
and reproducibility (Krippendorff 1980). Stability, the extent to which one annotator
will produce the same classifications at different times, is important because an unsta-
ble annotation scheme can never be reproducible. Reproducibility, the extent to which
different annotators will produce the same classifications, is important because it mea-
sures the consistency of shared understandings (or meaning) held between annotators.
work of the authors)?
Does this sentence refer to new, current
BACKGROUND
CONTRAST
YES NO
YES
NO
NOYES
YES
NO
YES
BASIS
NO
NO
YES
AIM
OWN
background, including phenomena
Does the sentence describe general
to be explained or linguistic example sentences?
that describes the specific aim
Does this sentence contain material
of the paper?
explicit reference to the
structure of the paper?
Does this sentence make
TEXTUAL
OTHER
or comparison of the own work to it?
of other work, or a contrast
Does it describe a negative aspect
or support for the current paper?
Does this sentence mention
other work as basis of 
work by the authors (excluding previous 
Figure 6
Decision tree for rhetorical annotation.
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Teufel and Moens Summarizing Scientific Articles
We use the kappa coefficient K (Siegel and Castellan 1988) to measure stability and
reproducibility, following Carletta (1996). The kappa coefficient is defined as follows:
K =
P(A) − P(E)
1 − P(E)
where P(A) is pairwise agreement and P(E) random agreement. K varies between 1
when agreement is perfect and −1 when there is a perfect negative correlation. K = 0
is defined as the level of agreement that would be reached by random annotation
using the same distribution of categories as the actual annotators did.
The main advantage of kappa as an annotation measure is that it factors out
random agreement by numbers of categories and by their distribution. As kappa also
abstracts over the number of annotators considered, it allows us to compare the agree-
ment numerically among a group of human annotators with the agreement between
the system and one or more annotators (section 5), which we use as one of the per-
formance measures of the system.
3.2.2 Results. The annotation experiments show that humans distinguish the seven
rhetorical categories with a stability of K = .82, .81, .76 (N = 1,220; k = 2, where K
stands for the kappa coefficient, N for the number of items (sentences) annotated, and
k for the number of annotators). This is equivalent to 93%, 92%, and 90% agreement.
Reproducibility was measured at K = .71 (N = 4,261, k = 3), which is equivalent
to 87% agreement. On Krippendorff’s (1980) scale, agreement of K = .8 or above is
considered as reliable, agreement of .67–.8 as marginally reliable, and less than .67 as
unreliable. On Landis and Koch’s (1977) more forgiving scale, agreement of .0–.2 is
considered as showing “slight” correlation, .21–.4 as “fair,” .41–.6 as “moderate,” .61–
0.8 as “substantial,” and .81 –1.0 as “almost perfect.” According to these guidelines,
our results can be considered reliable, substantial annotation.
Figure 7 shows that the distribution of the seven categories is very skewed, with
67% of all sentences being classified as OWN. (The distribution was calculated using
all three judgments per sentence [cf. the calculation of kappa]. The total number of
items is then k · N, i.e., 12,783 in this case.)
Table 2 shows a confusion matrix between two annotators. The numbers repre-
sent absolute sentence numbers, and the diagonal (boldface numbers) are the counts of
sentences that were identically classified by both annotators. We used Krippendorff’s
diagnostics to determine which particular categories humans had most problems with:
For each category, agreement is measured with a new data set in which all categories
Figure 7
Distribution of rhetorical categories (entire document).
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Computational Linguistics Volume 28, Number 4
Table 2
Confusion matrix between annotators B and C.
ANNOTATOR B
AIM CTR TXT OWN BKG BAS OTH Total
ANNOTATOR C AIM 35 2 1 19 3 2 62
CTR 86 31 16 23 156
TXT 31 7139
OWN 10 62 5 2,298 25 3 84 2,487
BKG 513115 20 153
BAS 218118 14 53
OTH 1 18 2 55 10 1 412 499
Total 48 173 39 2,441 170 22 556 3,449
except for the category of interest are collapsed into one metacategory. Original agree-
ment is compared to that measured on the new (artificial) data set; high values show
that annotators can distinguish the given category well from all others. When their re-
sults are compared to the overall reproducibility of K = .71, the annotators were good
at distinguishing AIM (Krippendorff’s diagnostics; K = .79) and TEXTUAL (K = .79).
The high agreement in AIM sentences is a positive result that seems to be at odds with
previous sentence extraction experiments. We take this as an indication that some types
of rhetorical classification are easier for human minds to do than unqualified relevance
decision. We also think that the positive results are partly due to the existence of the
guidelines.
The annotators were less consistent at determining BASIS (K = .49) and CONTRAST
(K = .59). The same picture emerges if we look at precision and recall of single cate-
gories between two annotators (cf. Table 3). Precision and recall for AIM and TEXTUAL
are high at 72%/56% and 79%/79%, whereas they are lower for CONTRAST (50%/55%)
and BASIS (82%/34%).
This contrast in agreement might have to do with the location of the rhetori-
cal zones in the paper: AIM and TEXTUAL zones are usually found in fixed locations
(beginning or end of the introduction section) and are explicitly marked with metadis-
course, whereas CONTRAST sentences, and even more so BASIS sentences, are usually
interspersed within longer OWN zones. As a result, these categories are more exposed
to lapses of attention during annotation.
With respect to the longer, more neutral zones (intellectual attribution), annotators
often had problems in distinguishing OTHER work from OWN work, particularly in
cases where the authors did not express a clear distinction between new work and
previous own work (which, according to our instructions, should be annotated as OTHER).
Another persistently problematic distinction for our annotators was that between OWN
Table 3
Annotator C’s precision and recall per category if annotator B is gold standard.
AIM CTR TXT OWN BKG BAS OTH
Precision 72% 50% 79% 94% 68% 82% 74%
Recall 56% 55% 79% 92% 75% 34% 83%
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Teufel and Moens Summarizing Scientific Articles
and BACKGROUND. This could be a sign that some authors aimed their papers at an
expert audience and thus thought it unnecessary to signal clearly which statements
are commonly agreed upon in the field, as opposed to their own new claims. If a
paper is written in such a way, it can indeed be understood only with a considerable
amount of domain knowledge, which our annotators did not have.
Because intellectual attribution (the distinction between OWN,OTHER, and BACK-
GROUND material) is an important part of our annotation scheme, we conducted a
second experiment measuring how well our annotators could distinguish just these
three roles, using the same annotators and 22 different articles. We wrote seven pages
of new guidelines describing the semantics of the three categories. Results show higher
stability compared to the full annotation scheme (K = .83, .79, .81; N = 1,248; k = 2)
and higher reproducibility (K = .78, N = 4,031, k = 3), corresponding to 94%, 93%,
and 93% agreement (stability) and 93% (reproducibility). It is most remarkable that
agreement of annotation of intellectual attribution in the abstracts is almost perfect:
K = .98 (N = 89, k = 3), corresponding to 99% agreement. This points to the fact that
authors, when writing abstracts for their papers, take care to make it clear to whom
a certain statement is attributed. This effect also holds for the annotation with the
full scheme with all seven categories: again, reproducibility in the abstract is higher
(K = .79) than in the entire document (K = .71), but the effect is much weaker.
Abstracts might be easier to annotate than the rest of a paper, but this does not
necessarily make it possible to define a gold standard solely by looking at the ab-
stracts. As foreshadowed in section 2.5, abstracts do not contain all types of rhetorical
information. AIM and OWN sentences make up 74% of the sentences in abstracts, and
only 5% of all CONTRAST sentences and 3% of all BASIS sentences occur in the abstract.
Abstracts in our corpus are also not structurally homogeneous. When we inspected
the rhetorical structure of abstracts in terms of sequences of rhetorical zones, we found
a high level of variation. Even though the sequence AIM–OWN is very common (con-
tained in 73% of all abstracts), the 80 abstracts still contain 40 different rhetorical
sequences, 28 of which are unique. This heterogeneity is in stark contrast to the sys-
tematic structures Liddy (1991) found to be produced by professional abstractors. Both
observations, the lack of certain rhetorical types in the abstracts and their rhetorical
heterogeneity, reassure us in our decision not to use human-written abstracts as a gold
standard.
3.3 Annotation of Relevance
We collected two different kinds of relevance gold standards for the documents in our
development corpus: abstract-similar document sentences and additional manually
selected sentences.
In order to establish alignment between summary and document sentences, we
used a semiautomatic method that relies on a simple surface similarity measure (long-
est common subsequence of content words, i.e., excluding words on a stop list). As in
Kupiec, Pedersen, and Chen’s experiment, final alignment was decided by a human
judge, and the criterion was semantic similarity of the two sentences. The following
sentence pair illustrates a direct match:
Summary: In understanding a reference, an agent determines his
confidence in its adequacy as a means of identifying the referent.
Document: An agent understands a reference once he is confident
in the adequacy of its (inferred) plan as a means of identifying the
referent.
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Computational Linguistics Volume 28, Number 4
Of the 346 abstract sentences contained in the 80 documents, 156 (45%) could be
aligned this way. Because of this low agreement and because certain rhetorical types
are not present in the abstracts, we decided not to rely on abstract alignment as our
only gold standard. Instead, we used manually selected sentences as an alternative
gold standard, which is more informative, but also more subjective.
We wrote eight pages of guidelines that describe relevance criteria (e.g., our defi-
nition prescribes that neutral descriptions of other work be selected only if the other
work is an essential part of the solution presented, whereas all statements of criti-
cism are to be included). The first author annotated all documents in the development
corpus for relevance using the rhetorical zones and abstract similarity as aides in the
relevance decision, and also skim-reading the whole paper before making the decision.
This resulted in 5 to 28 sentences per paper and a total of 1,183 sentences.
Implicitly, rhetorical classification of the extracted sentences was already given as
each of these sentences already had a rhetorical status assigned to it. However, the
rhetorical scheme we used for this task is slightly different. We excluded TEXTUAL,as
this category was designed for document uses other than summarization. If a selected
sentence had the rhetorical class TEXTUAL, it was reclassified into one of the other
six categories. Figure 8 shows the resulting category distribution among these 1,183
sentences, which is far more evenly distributed than the one covering all sentences (cf.
Figure 7). CONTRAST and OWN are the two most frequent categories.
We did not verify the relevance annotation with human experiments. We accept
that the set of sentences chosen by the human annotator is only one possible gold
standard. What is more important is that humans can agree on the rhetorical status of
the relevant sentences. Liddy observed that agreement on rhetorical status was easier
for professional abstractors than sentence selection: Although they did not necessarily
agree on which individual sentences should go into an abstract, they did agree on the
rhetorical information types that make up a good abstract.
We asked our trained annotators to classify a set of 200 sentences, randomly
sampled from the 1,183 sentences selected by the first author, into the six rhetori-
cal categories. The sentences were presented in order of occurrence in the document,
but without any context in terms of surrounding sentences. We measured stability at
K = .9, .86, .83 (N = 100, k = 2) and reproducibility at K = .84 (N = 200, k = 3). These
results are reassuring: They show that the rhetorical status for important sentences can
be particularly well determined, better than rhetorical status for all sentences in the
document (for which reproducibility was K = .71; cf. section 3.2.2).
Figure 8
Distribution of rhetorical categories (relevant sentences).
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Teufel and Moens Summarizing Scientific Articles
4. The System
We now describe an automatic system that can perform extraction and classification
of rhetorical status on unseen text (cf. also a prior version of the system reported
in Teufel and Moens [2000] and Teufel [1999]). We decided to use machine learning
to perform this extraction and classification, based on a variety of sentential features
similar to the ones reported in the sentence extraction literature. Human annotation is
used as training material such that the associations between these sentential features
and the target sentences can be learned. It is also used as gold standard for intrinsic
system evaluation.
A simpler machine learning approach using only word frequency information
and no other features, as typically used in tasks like text classification, could have
been employed (and indeed Nanba and Okumura [1999] do so for classifying citation
contexts). To test if such a simple approach would be enough, we performed a text
categorization experiment, using the Rainbow implementation of a na¨ıve Bayes term
frequency times inverse document frequency (TF*IDF) method (McCallum 1997) and
considering each sentence as a “document.” The result was a classification performance
of K = .30; the classifier nearly always chooses OWN and OTHER segments. The rare but
important categories AIM,BACKGROUND,CONTRAST, and BASIS could be retrieved only
with low precision and recall. Therefore, text classification methods do not provide a
solution to our problem. This is not surprising, given that the definition of our task has
little to do with the distribution of “content-bearing” words and phrases, much less so
than the related task of topic segmentation (Morris and Hirst 1991; Hearst 1997; Choi
2000), or Saggion and Lapalme’s (2000) approach to the summarization of scientific
articles, which relies on scientific concepts and their relations. Instead, we predict that
other indicators apart from the simple words contained in the sentence could provide
strong evidence for the modeling of rhetorical status. Also, the relatively small amount
of training material we have at our disposal requires a machine learning method that
makes optimal use of as many different kinds of features as possible. We predicted that
this would increase precision and recall on the categories in which we are interested.
The text classification experiment is still useful as it provides a nontrivial baseline for
comparison with our intrinsic system evaluation presented in section 5.
4.1 Classifiers
Weuseana¨ıve Bayesian model as in Kupiec, Pedersen, and Chen’s (1995) experiment
(cf. Figure 9). Sentential features are collected for each sentence (Table 4 gives an
overview of the features we used). Learning is supervised: In the training phase,
associations between these features and human-provided target categories are learned.
The target categories are the seven categories in the rhetorical annotation experiment
and relevant/nonrelevant in the relevance selection experiment. In the testing phase,
the trained model provides the probability of each target category for each sentence
of unseen text, on the basis of the sentential features identified for the sentence.
4.2 Features
Some of the features in our feature pool are unique to our approach, for instance, the
metadiscourse features. Others are borrowed from the text extraction literature (Paice
1990) or related tasks and adapted to the problem of determining rhetorical status.
Absolute location of a sentence. In the news domain, sentence location is the single
most important feature for sentence selection (Brandow, Mitze, and Rau 1995); in our
domain, location information, although less dominant, can still give a useful indication.
Rhetorical zones appear in typical positions in the article, as scientific argumentation
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Computational Linguistics Volume 28, Number 4
P(C | F
0
,..., F
n−1
) ≈ P(C)
producttext
n−1
j=0
P(F
j
| C)
producttext
n−1
j=0
P(F
j
)
P(C | F
0
, ..., F
n−1
): Probability that a sentence has target category C, given its feature val-
ues F
0
, ..., F
n−1
;
P(C): (Overall) probability of category C;
P(F
j
| C): Probability of feature-value pair F
j
, given that the sentence is of target
category C;
P(F
j
): Probability of feature value F
j
;
Figure 9
Na¨ıve Bayesian classifier.
Table 4
Overview of feature pool.
Type Name Feature Description Feature Values
Absolute
location
1. Loc Position of sentence in relation to 10
segments
A-J
Explicit
structure
2. Section
Struct
Relative and absolute position of
sentence within section (e.g., first
sentence in section or somewhere in
second third)
7 values
3. Para
Struct
Relative position of sentence within a
paragraph
Initial, Medial, Final
4. Headline Type of headline of current section 15 prototypical headlines
or Non-prototypical
Sentence
length
5. Length Is the sentence longer than a certain
threshold, measured in words?
YesorNo
Content
features
6. Title Does the sentence contain words also
occurring in the title or headlines?
YesorNo
7. TF*IDF Does the sentence contain “significant
terms” as determined by the TF*IDF
measure?
YesorNo
Verb syntax 8. Voice Voice (of first finite verb in sentence) Active or Passive or
NoVerb
9. Tense Tense (of first finite verb in sentence) 9 simple and complex
tenses or NoVerb
10. Modal Is the first finite verb modified by
modal auxiliary?
Modal or NoModal or
NoVerb
Citations 11. Cit Does the sentence contain a citation or
the name of an author contained in the
reference list? If it contains a citation,
is it a self-citation? Whereabouts in the
sentence does the citation occur?
{Citation (self), Citation
(other), Author Name,
or None}×{Beginning,
Middle, End}
History 12. History Most probable previous category 7 Target Categories +
“BEGIN”
Meta-
discourse
13. Formulaic Type of formulaic expression occur-
ring in sentence
18 Types of Formulaic
Expressions + 9 Agent
Types or None
14. Agent Type of agent 9 Agent Types or None
15. SegAgent Type of agent 9 Agent Types or None
16. Action Type of action, with or without
negation
27 Action Types or None
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Teufel and Moens Summarizing Scientific Articles
follows certain patterns (Swales 1990). For example, limitations of the author’s own
method can be expected to be found toward the end of the article, whereas limitations
of other researchers’ work are often discussed in the introduction. We observed that the
size of rhetorical zones depends on location, with smaller rhetorical zones occurring
toward the beginning and the end of the article. We model this by assigning location
values in the following fashion: The article is divided into 20 equal parts, counting
sentences. Sentences occurring in parts 1, 2, 3, 4, 19, and 20 receive the values A, B, C,
D, I, and J, respectively. Parts 5 and 6 are pooled, and sentences occurring in them are
given the value E; the same procedure is applied to parts 15 and 16 (value G) and 17
and 18 (value H). The remaining sentences in the middle (parts 7–14) all receive the
value F (cf. Figure 10).
Section structure. Sections can have an internal structuring; for instance, sentences
toward the beginning of a section often have a summarizing function. The section
location feature divides each section into three parts and assigns seven values: first
sentence, last sentence, second or third sentence, second-last or third-last sentence, or
else either somewhere in the first, second, or last third of the section.
Paragraph structure. In many genres, paragraphs also have internal structure (Wiebe
1994), with high-level or summarizing sentences occurring more often at the periphery
of paragraphs. In this feature, sentences are distinguished into those leading or ending
a paragraph and all others.
Headlines. Prototypical headlines can be an important predictor of the rhetorical
status of sentences occurring in the given section; however, not all texts in our collec-
tion use such headlines. Whenever a prototypical headline is recognized (using a set
of regular expressions), it is classified into one of the following 15 classes: Introduction,
Implementation, Example, Conclusion, Result, Evaluation, Solution, Experiment, Discussion,
Method, Problems, Related Work, Data, Further Work, Problem Statement. If none of the
patterns match, the value Non-Prototypical is assigned.
Sentence length. Kupiec, Pedersen, and Chen (1995) report sentence length as a
useful feature for text extraction. In our implementation, sentences are divided into
long or short sentences, by comparison to a fixed threshold (12 words).
Title word contents. Sentences containing many “content-bearing” words have been
hypothesized to be good candidates for text extraction. Baxendale (1958) extracted all
words except those on the stop list from the title and the headlines and determined
for each sentence whether or not it contained these words. We received better results
by excluding headline words and using only title words.
TF*IDF word contents. How content-bearing a word is can also be measured with
frequency counts (Salton and McGill 1983). The TF*IDF formula assigns high values
to words that occur frequently in one document, but rarely in the overall collection of
documents. We use the 18 highest-scoring TF*IDF words and classify sentences into
those that contain one or more of these words and those that do not.
Verb syntax. Linguistic features like tense and voice often correlate with rhetorical
zones; Biber (1995) and Riley (1991) show correlation of tense and voice with prototyp-
ical section structure (“method,” “introduction”). In addition, the presence or absence
BACDE F
GHIJ
1267 81 2 3 4 9 1011 13 141516 1718 19205
Figure 10
Values of location feature.
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Computational Linguistics Volume 28, Number 4
of a modal auxiliary might be relevant for detecting the phenomenon of “hedging”
(i.e., statements in which an author distances herself from her claims or signals low
certainty: these results might indicate that ...possibly ... [Hyland 1998]). For each sen-
tence, we use part-of-speech-based heuristics to determine tense, voice, and presence
of modal auxiliaries. This algorithm is shared with the metadiscourse features, and
the details are described below.
Citation. There are many connections between citation behavior and relevance or
rhetorical status. First, if a sentence contains a formal citation or the name of another
author mentioned in the bibliography, it is far more likely to talk about other work than
about own work. Second, if it contains a self-citation, it is far more likely to contain
a direct statement of continuation (25%) than a criticism (3%). Third, the importance
of a citation has been related to the distinction between authorial and parenthetical
citations. Citations are called authorial if they form a syntactically integral part of the
sentence or parenthetical if they do not (Swales 1990). In most cases, authorial citations
are used as the subject of a sentence, and parenthetical ones appear toward the middle
or the end of the sentence.
We built a recognizer for formal citations. It parses the reference list at the end of
the article and determines whether a citation is a self-citation (i.e., if there is an overlap
between the names of the cited researchers and the authors of the current paper), and
it also finds occurrences of authors’ names in running text, but outside of formal
citation contexts (e.g., Chomsky also claims that ...). The citation feature reports whether
a sentence contains an author name, a citation, or nothing. If it contains a citation,
the value records whether it is a self-citation and also records the location of the
citation in the sentence (in the beginning, the middle, or the end). This last distinction
is a heuristic for the authorial/parenthetical distinction. We also experimented with
including the number of different citations in a sentence, but this did not improve
results.
History. As there are typical patterns in the rhetorical zones (e.g., AIM sentences
tend to follow CONTRAST sentences), we wanted to include the category assigned to
the previous sentence as one of the features. In unseen text, however, the previous
target is unknown at training time (it is determined during testing). It can, however,
be calculated as a second pass process during training. In order to avoid a full Viterbi
search of all possibilities, we perform a beam search with width of three among the
candidates of the previous sentence, following Barzilay et al. (2000).
Formulaic expressions. We now turn to the last three features in our feature pool, the
metadiscourse features, which are more sophisticated than the other features. The first
metadiscourse feature models formulaic expressions like the ones described by Swales,
as they are semantic indicators that we expect to be helpful for rhetorical classification.
We use a list of phrases described by regular expressions, similar to Paice’s (1990)
grammar. Our list is divided into 18 semantic classes (cf. Table 5), comprising a total
of 644 patterns. The fact that phrases are clustered is a simple way of dealing with
data sparseness. In fact, our experiments in section 5.1.2 will show the usefulness
of our (manual) semantic clusters: The clustered list performs much better than the
unclustered list (i.e., when the string itself is used as a value instead of its semantic
class).
Agent. Agents and actions are more challenging to recognize. We use a mechanism
that, dependent on the voice of a sentence, recognizes agents (subjects or prepositional
phrases headed by by) and their predicates (“actions”). Classification of agents and
actions relies on a manually created lexicon of manual classes. As in the Formulaic
feature, similar agents and actions are generalized and clustered together to avoid data
sparseness.
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Teufel and Moens Summarizing Scientific Articles
Table 5
Formulaic expression lexicon.
Indicator Type Example Number
GAP INTRODUCTION to our knowledge 3
GENERAL FORMULAIC in traditional approaches 10
DEIXIS in this paper 11
SIMILARITY similar to 56
COMPARISON when compared to our 204
CONTRAST however 6
DETAIL this paper has also 4
METHOD a novel method for VERB–ing 33
PREVIOUS CONTEXT elsewhere, we have 25
FUTURE avenue for improvement 16
AFFECT hopefully 4
CONTINUATION following the argument in 19
IN ORDER TO in order to 1
POSITIVE ADJECTIVE appealing 68
NEGATIVE ADJECTIVE unsatisfactory 119
THEM FORMULAIC along the lines of 6
TEXTSTRUCTURE in section 3 16
NO TEXTSTRUCTURE described in the last section 43
Total of 18 classes 644
Table 6
Agent lexicon.
Agent Type Example Number Removed
US AGENT we 22
THEM AGENT his approach 21
GENERAL AGENT traditional methods 20 X
US PREVIOUS AGENT the approach in SELFCITE 7
OUR AIM AGENT the point of this study 23
REF US AGENT this method (this WORK NOUN)6
REF AGENT the paper 11 X
THEM PRONOUN AGENT they 1X
AIM REF AGENT its goal 8
GAP AGENT none of these papers 8
PROBLEM AGENT these drawbacks 3X
SOLUTION AGENT a way out of this dilemma 4X
TEXTSTRUCTURE AGENT the concluding chapter 33
Total of 13 classes 167
The lexicon for agent patterns (cf. Table 6) contains 13 types of agents and a total
of 167 patterns. These 167 patterns expand to many more strings as we use a replace
mechanism (e.g., the placeholder WORK NOUN in the sixth row of Table 6 can be
replaced by a set of 37 nouns including theory, method, prototype, algorithm).
The main three agent types we distinguish are US AGENT, THEM AGENT, and GEN-
ERAL AGENT, following the types of intellectual attribution discussed above. A fourth
type is US PREVIOUS AGENT (the authors, but in a previous paper).
Additional agent types include nonpersonal agents like aims, problems, solu-
tions, absence of solution, or textual segments. There are four equivalence classes of
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Computational Linguistics Volume 28, Number 4
agents with ambiguous reference (“this system”): REF AGENT, REF US AGENT, THEM PRO-
NOUN AGENT, and AIM REF AGENT.
Agent classes were created based on intuition, but subsequently each class was
tested with corpus statistics to determine whether it should be removed or not. We
wanted to find and exclude classes that had a distribution very similar to the overall
distribution of the target categories, as such features are not distinctive. We measured
associations using the log-likelihood measure (Dunning 1993) for each combination of
target category and semantic class by converting each cell of the contingency into a 2×2
contingency table. We kept only classes of verbs in which at least one category showed
a high association (gscore > 5.0), as that means that in these cases the distribution was
significantly different from the overall distribution. The last column in Table 6 shows
that the classes THEM PRONOUN, GENERAL, SOLUTION, PROBLEM, and REF were removed;
removal improved the performance of the Agent feature.
SegAgent. SegAgent is a variant of the Agent feature that keeps track of previously
recognized agents; unmarked sentences receive these previous agents as a value (in
the Agent feature, they would have received the value None).
Action. We use a manually created action lexicon containing 365 verbs (cf. Table 7).
The verbs are clustered into 20 classes based on semantic concepts such as similarity,
contrast, competition, presentation, argumentation, and textual structure. For exam-
ple, PRESENTATION ACTIONs include communication verbs like present, report, and state
(Myers 1992; Thompson and Yiyun 1991), RESEARCH ACTIONS include analyze, conduct,
define and observe, and ARGUMENTATION ACTIONS include argue, disagree, and object to.
Domain-specific actions are contained in the classes indicating a problem (fail, degrade,
waste, overestimate) and solution-contributing actions (circumvent, solve, mitigate). The
Table 7
Action lexicon.
Action Type Example Number Removed
AFFECT we hope to improve our results 9X
ARGUMENTATION we argue against a model of 19 X
AWARENESS we are not aware of attempts 5+
BETTER SOLUTION our system outperforms ... 9 −
CHANGE we extend CITE’s algorithm 23
COMPARISON we tested our system against ... 4
CONTINUATION we follow CITE ... 13
CONTRAST our approach differs from ... 12 −
FUTURE INTEREST we intend to improve ... 4X
INTEREST we are concerned with ... 28
NEED this approach, however, lacks ... 8X
PRESENTATION we present here a method for ... 19 −
PROBLEM this approach fails ... 61 −
RESEARCH we collected our data from ... 54
SIMILAR our approach resembles that of 13
SOLUTION we solve this problem by ... 64
TEXTSTRUCTURE the paper is organized ... 13
USE we employ CITE’s method ... 5
COPULA our goal is to ... 1
POSSESSION we have three goals ... 1
Total of 20 classes 365
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recognition of negation is essential; the semantics of not solving is closer to being prob-
lematic than it is to solving.
The following classes were removed by the gscore test described above, because
their distribution was too similar to the overall distribution: FUTURE INTEREST, NEED,
ARGUMENTATION, AFFECT in both negative and positive contexts (X in last column of
Table 7), and AWARENESS only in positive context (+ in last column). The following
classes had too few occurrences in negative context (< 10 occurrences in the whole verb
class) and thus the negative context of the class was also removed: BETTER SOLUTION,
CONTRAST, PRESENTATION, PROBLEM (− in last column). Again, the removal improved
the performance of the Action feature.
The algorithm for determining agents and actions relies on finite-state patterns
over part-of-speech (POS) tags. Starting from each finite verb, the algorithm collects
chains of auxiliaries belonging to the associated finite clause and thus determines the
clause’s tense and voice. Other finite verbs and commas are assumed to be clause
boundaries. Once the semantic verb is found, its stem is looked up in the action
lexicon. Negation is determined if one of 32 fixed negation words is present in a
six-word window to the right of the finite verb.
As our classifier requires one unique value for each classified item for each feature,
we had to choose one value for sentences containing more than one finite clause. We
return the following values for the action and agents feature: the first agent/action
pair, if both are nonzero, otherwise the first agent without an action, otherwise the
first action without an agent, if available.
In order to determine the level of correctness of agent and action recognition, we
had first to evaluate manually the error level of the POS tagging of finite verbs, as our
algorithm crucially relies on finite verbs. In a random sample of 100 sentences from
our development corpus that contain any finite verbs at all (they happened to contain
a total of 184 finite verbs), the tagger (which is part of the TTT software) showed a
recall of 95% and a precision of 93%.
We found that for the 174 correctly determined finite verbs, the heuristics for
negation and presence of modal auxiliaries worked without any errors (100% accu-
racy, eight negated sentences). The correct semantic verb was determined with 96%
accuracy; most errors were due to misrecognition of clause boundaries. Action Type
lookup was fully correct (100% accuracy), even in the case of phrasal verbs and longer
idiomatic expressions (have to is a NEED ACTION; be inspired by is a CONTINUE ACTION).
There were seven voice errors, two of which were due to POS-tagging errors (past par-
ticiple misrecognized). The remaining five voice errors correspond to 98% accuracy.
Correctness of Agent Type determination was tested on a random sample of 100
sentences containing at least one agent, resulting in 111 agents. No agent pattern that
should have been identified was missed (100% recall). Of the 111 agents, 105 cases were
correct (precision of 95%). Therefore, we consider the two features to be adequately
robust to serve as sentential features in our system.
Having detailed the features and classifiers of the machine learning system we
use, we will now turn to an intrinsic evaluation of its performance.
5. Intrinsic System Evaluation
Our task is to perform content selection from scientific articles, which we do by clas-
sifying sentences into seven rhetorical categories. The summaries based on this classi-
fication use some of these sentences directly, namely, sentences that express the con-
tribution of a particular article (AIM), sentences expressing contrasts with other work
(CONTRAST), and sentences stating imported solutions from other work (BASIS). Other,
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more frequent rhetorical categories, namely OTHER,OWN, and BACKGROUND, might
also be extracted into the summary.
Because the task is a mixture of extraction and classification, we report system
performance as follows:
• We first report precision and recall values for all categories, in
comparison to human performance and the text categorization baseline,
as we are primarily interested in good performance on the categories
AIM,CONTRAST,BASIS, and BACKGROUND.
• We are also interested in good overall classification performance, which
we report using kappa and macro-F as our metric. We also discuss how
well each single features does in the classification.
• We then compare the extracted sentences to our human gold standard
for relevance and report the agreement in precision and agreement per
category.
5.1 Determination of Rhetorical Status
The results of stochastic classification were compiled with a 10-fold cross-validation on
our 80-paper corpus. As we do not have much annotated material, cross-validation is
a practical way to test as it can make use of the full development corpus for training,
without ever using the same data for training and testing.
5.1.1 Overall Results. Table 8 and Figure 11 show that the stochastic model obtains
substantial improvement over the baseline in terms of precision and recall of the im-
portant categories AIM,BACKGROUND,CONTRAST, and BASIS. We use the F-measure,
defined by van Rijsbergen (1979) as
2PR
P+R
, as a convenient way of reporting precision (P)
and recall (R) in one value. F-measures for our categories range from .61 (TEXTUAL)
and .52 (AIM) to .45 (BACKGROUND), .38 (BASIS), and .26 (CONTRAST). The recall for
some categories is relatively low. As our gold standard is designed to contain a lot
of redundant information for the same category, this is not too worrying. Low pre-
cision in some categories (e.g., 34% for CONTRAST, in contrast to human precision of
50%), however, could potentially present a problem for later steps in the document
summarization process.
Overall, we find these results encouraging, particularly in view of the subjective
nature of the task and the high compression achieved (2% for AIM,BASIS, and TEXTUAL
sentences, 5% for CONTRAST sentences, and 6% for BACKGROUND sentences). No direct
comparison with Kupiec, Pedersen, and Chen’s results is possible as different data
sets are used and as Kupiec et al.’s relevant sentences do not directly map into one
of our categories. Assuming, however, that their relevant sentences are probably most
Table 8
Performance per category: F-measure (F), precision (P) and recall (R).
AIM CONTR. TEXTUAL OWN BACKG. BASIS OTHER
FPR FPR FP R FPR FPR FPR FP R
System 52 44 65 26 34 20 61 57 66 86 84 88 45 40 50 38 37 40 44 52 39
Baseline 11 30 7 17 31 12 23 56 15 83 78 90 22 32 17 7 15 5 44 47 42
Humans 63 72 56 52 50 55 79 79 79 93 94 92 71 68 75 48 82 34 78 74 83
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Figure 11
Performance per category: F-measure.
Table 9
Confusion matrix: Human versus automatic annotation.
MACHINE
AIM CTR TXT OWN BKG BAS OTH Total
HUMAN AIM 127 6 13 23 19 5 10 203
CTR 21 112 4 204 87 18 126 572
TXT 14 1 145 46 6 2 6 220
OWN 100 108 84 7,231 222 71 424 8,240
BKG 14 31 1 222 370 5 101 744
BAS 17 7 7 60 8 97 39 235
OTH 6 70 10 828 215 72 773 1,974
Total 299 335 264 8,614 927 270 1,479 12,188
comparable to our AIM sentences, our precision and recall of 44% and 65% compare
favorably to theirs (42% and 42%).
Table 9 shows a confusion matrix between one annotator and the system. The
system is likely to confuse AIM and OWN sentences (e.g., 100 out of 172 sentences
incorrectly classified as AIM by the system turned out to be OWN sentences). It also
shows a tendency to confuse OTHER and OWN sentences. The system also fails to dis-
tinguish categories involving other people’s work (e.g. OTHER,BASIS, and CONTRAST).
Overall, these tendencies mirror human errors, as can be seen from a comparison with
Table 2.
Table 10 shows the results in terms of three overall measures: kappa, percentage
accuracy, and macro-F (following Lewis [1991]). Macro-F is the mean of the F-measures
of all seven categories. One reason for using macro-F and kappa is that we want to
measure success particularly on the rare categories that are needed for our final task
(i.e., AIM,BASIS, and CONTRAST). Microaveraging techniques like traditional accuracy
tend to overestimate the contribution of frequent categories in skewed distributions
like ours; this is undesirable, as OWN is the least interesting category for our purposes.
This situation has parallels in information retrieval, where precision and recall are used
because accuracy overestimates the performance on irrelevant items.
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Table 10
Overall classification results.
System/Baseline Compared with One Human Annotator 3 Humans
System Text Class. Random Random (Distr.) Most Freq.
Kappa .45 .30 −.10 0 −.13 .71
Accuracy .73 .72 .14 .48 .67 .87
Macro-F .50 .30 .09 .14 .11 .69
In the case of macro-F, each category is treated as one unit, independent of the
number of items contained in it. Therefore, the classification success of the individual
items in rare categories is given more importance than the classification success of
frequent-category items. When looking at the numerical values, however, one should
keep in mind that macroaveraging results are in general numerically lower (Yang and
Liu 1999). This is because there are fewer training cases for the rare categories, which
therefore perform worse with most classifiers.
In the case of kappa, classifications that incorrectly favor frequent categories are
punished because of a high random agreement. This effect can be shown most easily
when the baselines are considered. The most ambitious baseline we use is the output of
a text categorization system, as described in section 4. Other possible baselines, which
are all easier to beat, include classification by the most frequent category. This baseline
turns out to be trivial, as it does not extract any of the rare rhetorical categories in which
we are particularly interested, and therefore receives a low kappa value at K = −.12.
Possible chance baselines include random annotation with uniform distribution (K =
−.10; accuracy of 14%) and random annotation with observed distribution. The latter
baseline is built into the definition of kappa (K = 0; accuracy of 48%).
Although our system outperforms an ambitious baseline (macro-F shows that our
system performs roughly 20% better than text classification) and also performs much
above chance, there is still a big gap in performance between humans and machine.
Macro-F shows a 20% difference between our system and human performance. If
the system is put into a pool of annotators for the 25 articles for which three-way
human judgment exists, agreement drops from K = .71 to K = .59. This is a clear
indication that the system’s annotation is still distinguishably different from human
annotation.
5.1.2 Feature Impact. The previous results were compiled using all features, which is
the optimal feature combination (as determined by an exhaustive search in the space
of feature combinations). The most distinctive single feature is Location (achieving an
agreement of K = .22 against one annotator, if this feature is used as the sole feature),
followed by SegAgent (K = .19), Citations (K = .18), Headlines (K = .17), Agent
(K = .08), and Formulaic (K = .07). In each case, the unclustered versions of Agent,
SegAgent, and Formulaic performed much worse than the clustered versions; they
did not improve final results when added into the feature pool.
Action performs slightly better at K = −.11 than the baseline by most frequent
category, but far worse than random by observed distribution. The following features
on their own classify each sentence as OWN (and therefore achieve K = −.12): Relative
Location,Paragraphs,TF*IDF,Title,Sentence Length,Modality,Tense , and
Voice. History performs very badly on its own at K = −.51; it classifies almost all
sentences as BACKGROUND. It does this because the probability of the first sentence’s
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being a BACKGROUND sentence is almost one, and, if no other information is available,
it is very likely that another BACKGROUND sentence will follow after a BACKGROUND
sentence.
Each of these features, however, still contributes to the final result: If any of them
is taken out of the feature pool, classification performance decreases. How can this be,
given that the individual features perform worse than chance? As the classifier de-
rives the posterior probability by multiplying evidence from each feature, even slight
evidence coming from one feature can direct the decision in the right direction. A
feature that contributes little evidence on its own (too little to break the prior prob-
ability, which is strongly biased toward OWN) can thus, in combination with others,
still help in disambiguating. For the na¨ıve Bayesian classification method, indeed, it
is most important that the features be as independent of each other as possible. This
property cannot be assessed by looking at the feature’s isolated performance, but only
in combination with others.
It is also interesting to see that certain categories are disambiguated particularly
well by certain features (cf. Table 11). The Formulaic feature, which is by no means
the strongest feature, is nevertheless the most diverse, as it contributes to the disam-
biguation of six categories directly. This is because many different rhetorical categories
have typical cue phrases associated with them (whereas not all categories have a pre-
ferred location in the document). Not surprisingly, Location and History are the
features particularly useful for detecting BACKGROUND sentences, and SegAgent addi-
tionally contributes toward the determination of BACKGROUND zones (along with the
Formulaic and the Absolute Location features). The Agent and Action features also
prove their worth as they manage to disambiguate categories that many of the other
features alone cannot disambiguate (e.g., CONTRAST).
5.1.3 System Output: The Example Paper. In order to give the reader an impression
of how the figures reported in the previous section translate into real output, we
present in figure 12 the output of the system when run on the example paper (all
AIM,CONTRAST, and BASIS sentences). The second column shows whether the human
judge agrees with the system’s decision (a tick for correct decisions, and the human’s
preferred category for incorrect decisions). Ten out of the 15 extracted sentences have
been classified correctly.
The example also shows that the determination of rhetorical status is not always
straightforward. For example, whereas the first AIM sentence that the system proposes
(sentence 8) is clearly wrong, all other “incorrect” AIM sentences carry important in-
Table 11
Precision and recall of rhetorical classification, individual features.
Features Precision/Recall per Category (in %)
AIM CONTR. TXT. OWN BACKG. BASIS OTHER
SegAgent alone — 17/0 — 74/94 53/16 — 46/33
Agent alone — — — 71/93 — — 36/23
Location alone — — — 74/97 40/36 — 28/9
Headlines alone — — — 75/95 — — 29/25
Citation alone — — — 73/96 — — 43/30
Formulaic alone 40/2 45/2 75/39 71/98 — 40/1 47/13
Action alone — 43/1 — 68/99 — — —
History alone — — —- 70/8 16/99 — —
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System Human
AIM (OTH) 8 In Hindle’s proposal, words are similar if we have strong statistical
evidence that they tend to participate in the same events.
√
*10 Our research addresses some of the same questions and uses similar raw
data, but we investigate how to factor word association tendencies into
associations of words to certain hidden senses classes and associations
between the classes themselves.
√
11 While it may be worthwhile to base such a model on preexisting sense
classes (Resnik, 1992), in the work described here we look at how to
derive the classes directly from distributional data.
(OWN) 12 More specifically, we model senses as probabilistic concepts or clusters
c with corresponding cluster membership probabilities EQN for each
word w.
√
*22 We will consider here only the problem of classifying nouns according
to their distribution as direct objects of verbs; the converse problem is
formally similar.
(CTR) 41 However, this is not very satisfactory because one of the goals of our
work is precisely to avoid the problems of data sparseness by grouping
words into classes.
(OWN) 150 We also evaluated asymmetric cluster models on a verb decision task
closer to possible applications to disambiguation in language analysis.
√
* 162 We have demonstrated that a general divisive clustering procedure for
probability distributions can be used to group words according to their
participation in particular grammatical relations with other words.
BAS
√
19 The corpus used in our first experiment was derived from newswire text
automatically parsed by Hindle’s parser Fidditch (Hindle, 1993).
√
20 More recently, we have constructed similar tables with the help of a
statistical part-of-speech tagger (Church, 1988) and of tools for regular
expression pattern matching on tagged corpora (Yarowsky, 1992).
√
*113 The analogy with statistical mechanics suggests a deterministic anneal-
ing procedure for clustering (Rose et al., 1990), in which the number
of clusters is determined through a sequence of phase transitions by
continuously increasing the parameter EQN following an annealing
schedule.
CTR
√
*9 His notion of similarity seems to agree with our intuitions in many
cases, but it is not clear how it can be used directly to construct word
classes and corresponding models of association.
√
*14 Class construction is then combinatorially very demanding and de-
pends on frequency counts for joint events involving particular words,
a potentially unreliable source of information as we noted above.
(OWN) 21 We have not yet compared the accuracy and coverage of the two methods,
or what systematic biases they might introduce, although we took care
to filter out certain systematic errors, for instance the misparsing of the
subject of a complement clause as the direct object of a main verb for
report verbs like “say”.
√
43 This is a useful advantage of our method compared with agglomerative
clustering techniques that need to compare individual objects being
considered for grouping.
Figure 12
System output for example paper.
formation about research goals of the paper: Sentence 41 states the goal in explicit
terms, but it also contains a contrastive statement, which the annotator decided to rate
higher than the goal statement. Both sentences 12 and 150 give high-level descrip-
tions of the work that might pass as a goal statement. Similarly, in sentence 21 the
agent and action features detected that the first part of the sentence has something to
do with comparing methods, and the system then (plausibly but incorrectly) decided
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Teufel and Moens Summarizing Scientific Articles
to classify the sentence as CONTRAST. All in all, we feel that the extracted material
conveys the rhetorical status adequately. An extrinsic evaluation additionally showed
that the end result provides considerable added value when compared to sentence
extracts (Teufel 2001).
5.2 Relevance Determination
The classifier for rhetorical status that we evaluated in the previous section is an im-
portant first step in our implementation; the next step is the determination of relevant
sentences in the text. One simple solution for relevance decision would be to use all
AIM,BASIS, and CONTRAST sentences, as these categories are rare overall. The classifier
we use has the nice property of roughly keeping the distribution of target categories,
so that we end up with a sensible number of these sentences.
The strategy of using all AIM,CONTRAST, and BASIS sentences can be evaluated
in a similar vein to the previous experiment. In terms of relevance, the asterisk in
figure 12 marks sentences that the human judge found particularly relevant in the
overall context (cf. the full set in figure 5). Six out of all 15 sentences, and 6 out of
the 10 sentences that received the correct rhetorical status, were judged relevant in the
example.
Table 12 reports the figure for the entire corpus by comparing the system’s output
of correctly classified rhetorical categories to human judgment. In all cases, the results
are far above the nontrivial baseline. On AIM,CONTRAST, and BASIS sentences, our
system achieves very high precision values of 96%, 70%, and 71%. Recall is lower at
70%, 24%, and 39%, but low recall is less of a problem in our final task. Therefore,
the main bottleneck is correct rhetorical classification. Once that is accomplished, the
selected categories show high agreement with human judgment and should therefore
represent good material for further processing steps.
If, however, one is also interested in selecting BACKGROUND sentences, as we are,
simply choosing all BACKGROUND sentences would result in low precision of 16%
(albeit with a high recall of 83%), which does not seem to be the optimal solution.
We therefore use a second classifier for finding the most relevant sentences indepen-
dently that was trained on the relevance gold standard. Our best classifier operates
at a precision of 46.5% and recall of 45.2% (using the features Location,Section
Struct,Paragraph Struct,Title,TF*IDF,Formulaic , and Citation for classifi-
cation). The second classifier (cf. rightmost columns in figure 12) raises the preci-
sion for BACKGROUND sentences from 16% to 38%, while keeping recall high at 88%.
This example shows that the right procedure for relevance determination changes
from category to category and also depends on the final task one is trying
to accomplish.
Table 12
Relevance by human selection: Precision (P) and recall (R).
AIM CONTR. BASIS BACKGROUND
Without With
Classifier Classifier
PRPRPRPRPR
System 96.2 69.8 70.1 23.8 70.5 39.4 16.0 83.3 38.4 88.2
Baseline 26.1 6.4 23.5 14.4 6.94 2.7 0.0 0.0 0.0 0.0
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6. Discussion
6.1 Contribution
We have presented a new method for content selection from scientific articles. The anal-
ysis is genre-specific; it is based on rhetorical phenomena specific to academic writing,
such as problem-solution structure, explicit intellectual attribution, and statements of
relatedness to other work. The goal of the analysis is to identify the contribution of
an article in relation to background material and to other specific current work.
Our methodology is situated between text extraction methods and fact extraction
(template-filling) methods: Although our analysis has the advantage of being more
context-sensitive than text extraction methods, it retains the robustness of this approach
toward different subdomains, presentational traditions, and writing styles.
Like fact extraction methods (e.g., Radev and McKeown 1998), our method also
uses a “template” whose slots are being filled during analysis. The slots of our template
are defined as rhetorical categories (like “Contrast”) rather than by domain-specific
categories (like “Perpetrator”). This makes it possible for our approach to deal with
texts of different domains and unexpected topics.
Sparck Jones (1999) argues that it is crucial for a summarization strategy to relate
the large-scale document structure of texts to readers’ tasks in the real world (i.e., to
the proposed use of the summaries). We feel that incorporating a robust analysis of
discourse structure into a document summarizer is one step along this way.
Our practical contributions are twofold. First, we present a scheme for the anno-
tation of sentences with rhetorical status, and we have shown that the annotation is
stable (K = .82, .81, .76) and reproducible (K = .71). Since these results indicate that
the annotation is reliable, we use it as our gold standard for evaluation and training.
Second, we present a machine learning system for the classification of sentences by
relevance and by rhetorical status. The contribution here is not the statistical classifier,
which is well-known and has been used in a similar task by Kupiec, Pedersen, and
Oren (1995), but instead the features we use. We have adapted 13 sentential features
in such a way that they work robustly for our task (i.e., for unrestricted, real-world
text). We also present three new features that detect scientific metadiscourse in a novel
way. The results of an intrinsic system evaluation show that the system can identify
sentences expressing the specific goal of a paper with 57% precision and 79% recall,
sentences expressing criticism or contrast with 57% precision and 42% recall, and
sentences expressing a continuation relationship to other work with 62% precision
and 43% recall. This substantially improves a baseline of text classification which uses
only a TF*IDF model over words. The agreement of correctly identified rhetorical roles
with human relevance judgments is even higher (96% precision and 70% recall for goal
statements, 70% precision and 24% recall for contrast, 71% precision and 39% recall for
continuation). We see these results as an indication that shallow discourse processing
with a well-designed set of surface-based indicators is possible.
6.2 Limitations and Future Work
The metadiscourse features, one focus of our work, currently depend on manual re-
sources. The experiments reported here explore whether metadiscourse information is
useful for the automatic determination of rhetorical status (as opposed to more shallow
features), and this is clearly the case. The next step, however, should be the automatic
creation of such resources. For the task of dialogue act disambiguation, Samuel, Car-
berry, and Vijay-Shanker (1999) suggest a method of automatically finding cue phrases
for disambiguation. It may be possible to apply this or a similar method to our data
and to compare the performance of automatically gained resources with manual ones.
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Further work can be done on the semantic verb clusters described in section 4.2.
Klavans and Kan (1998), who use verb clusters for document classification according to
genre, observe that verb information is rarely used in current practical natural language
applications. Most tasks such as information extraction and document classification
identify and use nominal constructs instead (e.g., noun phrases, TF*IDF words and
phrases).
The verb clusters we employ were created using our intuition of which type of
verb similarity would be useful in the genre and for the task. There are good reasons
for using such a hand-crafted, genre-specific verb lexicon instead of a general resource
such as WordNet or Levin’s (1993) classes: Many verbs used in the domain of scien-
tific argumentation have assumed a specialized meaning, which our lexicon readily
encodes. Klavans and Kan’s classes, which are based on Levin’s classes, are also man-
ually created. Resnik and Diab (2000) present yet other measures of verb similarity,
which could be used to arrive at a more data-driven definition of verb classes. We
are currently comparing our verb clusterings to Klavans and Kan’s, and to bottom-up
clusters of verb similarities generated from our annotated data.
The recognition of agents, which is already the second-best feature in the pool,
could be further improved by including named entity recognition and anaphora res-
olution. Named entity recognition would help in cases like the following,
LHIP provides a processing method which allows selected portions of the input
to be ignored or handled differently. (S-5, 9408006)
where LHIP is the name of the authors’ approach and should thus be tagged as
US AGENT; to do so, however, one would need to recognize it as a named approach,
which is associated with the authors. It is very likely that such a treatment, which
would have to include information from elsewhere in the text, would improve re-
sults, particularly as named approaches are frequent in the computational linguistics
domain. Information about named approaches in themselves would also be an impor-
tant aspect to include in summaries or citation indexes.
Anaphora resolution helps in cases in which the agent is syntactically ambiguous
between own and other approaches (e.g., this system). To test whether and how much
performance would improve, we manually simulated anaphora resolution on the 632
occurrences of REF AGENT in the development corpus. (In the experiments in section 5
these occurrences had been excluded from the Agent feature by giving them the value
None; we include them now in their disambiguated state). Of the 632 REF AGENTs,
436 (69%) were classified as US AGENT, 175 (28%) as THEM AGENT, and 20 (3%) as
GENERAL AGENT. As a result of this manual disambiguation, the performance of the
Agent feature increased dramatically from K = .08 to K = .14 and that of SegAgent
from K = .19 to K = .22. This is a clear indication of the potential added value of
anaphora resolution for our task.
As far as the statistical classification is concerned, our results are still far from
perfect. Obvious ways of improving performance are the use of a more sophisticated
statistical classifier and more training material. We have experimented with a maxi-
mum entropy model, Repeated Incremental Pruning to Produce Error Reduction (RIP-
PER), and decision trees; preliminary results do not show significant improvement
over the na¨ıve Bayesian model. One problem is that 4% of the sentences in our cur-
rent annotated material are ambiguous: They receive the same feature representation
but are classified differently by the annotators. A possible solution is to find better
and more distinctive features; we believe that robust, higher-level features like actions
and agents are a step in the right direction. We also suspect that a big improvement
440
Computational Linguistics Volume 28, Number 4
could be achieved with smaller annotation units. Many errors come from instances in
which one half of a sentence serves one rhetorical purpose, the other another, as in
the following example:
The current paper shows how to implement this general notion, without fol-
lowing Krifka’s analysis in detail. (S-10, 9411019)
Here, the first part describes the paper’s research goal, whereas the second expresses a
contrast. Currently, one target category needs to be associated with the whole sentence
(according to a rule in the guidelines, AIM is given preference over CONTRAST). As
an undesired side effect, the CONTRAST-like textual parts (and the features associated
with this text piece, e.g., the presence of an author’s name) are wrongly associated
with the AIM target category. If we allowed for a smaller annotation unit (e.g., at the
clause level), this systematic noise in the training data could be removed.
Another improvement in classification accuracy might be achieved by performing
the classification in a cascading way. The system could first perform a classification
into OWN-like classes (OWN,AIM, and TEXTUAL pooled), OTHER-like categories (OTHER,
CONTRAST, and BASIS pooled), and BACKGROUND, similar to the way human annotation
proceeds. Subclassification among these classes would then lead to the final seven-way
classification.
Appendix: List of articles in CL development corpus
No. Title, Conference, Authors
0 9405001 Similarity-Based Estimation of Word Cooccurrence Probabilities (ACL94), I. Dagan et al.
1 9405002 Temporal Relations: Reference or Discourse Coherence? (ACL94 Student), A. Kehler
2 9405004 Syntactic-Head-Driven Generation (COLING94), E. Koenig
3 9405010 Common Topics and Coherent Situations: Interpreting Ellipsis in the Context of Dis-
course Inference (ACL94), A. Kehler
4 9405013 Collaboration on Reference to Objects That Are Not Mutually Known (COLING94),
P. Edmonds
5 9405022 Grammar Specialization through Entropy Thresholds (ACL94), C. Samuelsson
6 9405023 An Integrated Heuristic Scheme for Partial Parse Evaluation (ACL94 Student), A. Lavie
7 9405028 Semantics of Complex Sentences in Japanese (COLING94), H. Nakagawa, S. Nishizawa
8 9405033 Relating Complexity to Practical Performance in Parsing with Wide-Coverage Unifica-
tion Grammars (ACL94), J. Carroll
9 9405035 Dual-Coding Theory and Connectionist Lexical Selection (ACL94 Student), Y. Wang
10 9407011 Discourse Obligations in Dialogue Processing (ACL94), D. Traum, J. Allen
11 9408003 Typed Feature Structures as Descriptions (COLING94 Reserve), P. King
12 9408004 Parsing with Principles and Probabilities (ACL94 Workshop), A. Fordham, M. Crocker
13 9408006 LHIP: Extended DCGs for Configurable Robust Parsing (COLING94), A. Bal-
lim, G. Russell
14 9408011 Distributional Clustering of English Words (ACL93), F. Pereira et al.
15 9408014 Qualitative and Quantitative Models of Speech Translation (ACL94 Workshop),
H. Alshawi
16 9409004 An Experiment on Learning Appropriate Selectional Restrictions from a Parsed Corpus
(COLING94), F. Ribas
17 9410001 Improving Language Models by Clustering Training Sentences (ANLP94), D. Carter
18 9410005 A Centering Approach to Pronouns (ACL87), S. Brennan et al.
19 9410006 Evaluating Discourse Processing Algorithms (ACL89), M. Walker
20 9410008 Recognizing Text Genres with Simple Metrics Using Discriminant Analysis (COLING94),
J. Karlgren, D. Cutting
21 9410009 Reserve Lexical Functions and Machine Translation (COLING94), D. Heylen et al.
22 9410012 Does Baum-Welch Re-estimation Help Taggers? (ANLP94), D. Elworthy
23 9410022 Automated Tone Transcription (ACL94 SIG), S. Bird
24 9410032 Planning Argumentative Texts (COLING94), X. Huang
25 9410033 Default Handling in Incremental Generation (COLING94), K. Harbusch et al.
26 9411019 Focus on “Only” and “Not” (COLING94), A. Ramsay
27 9411021 Free-Ordered CUG on Chemical Abstract Machine (COLING94), S. Tojo
441
Teufel and Moens Summarizing Scientific Articles
28 9411023 Abstract Generation Based on Rhetorical Structure Extraction (COLING94), K. Ono et
al.
29 9412005 Segmenting Speech without a Lexicon: The Roles of Phonotactics and Speech Source
(ACL94 SIG), T. Cartwright, M. Brent
30 9412008 Analysis of Japanese Compound Nouns Using Collocational Information (COLING94),
Y. Kobayasi et al.
31 9502004 Bottom-Up Earley Deduction (COLING94), G. Erbach
32 9502005 Off-Line Optimization for Earley-Style HPSG Processing (EACL95), G. Minnen et al.
33 9502006 Rapid Development of Morphological Descriptions for Full Language Processing Sys-
tems (EACL95), D. Carter
34 9502009 On Learning More Appropriate Selectional Restrictions (EACL95), F. Ribas
35 9502014 Ellipsis and Quantification: A Substitutional Approach (EACL95), R. Crouch
36 9502015 The Semantics of Resource Sharing in Lexical-Functional Grammar (EACL95), A. Kehler
et al.
37 9502018 Algorithms for Analysing the Temporal Structure of Discourse (EACL95), J. Hitzeman
et al.
38 9502021 A Tractable Extension of Linear Indexed Grammars (EACL95), B. Keller, D. Weir
39 9502022 Stochastic HPSG (EACL95), C. Brew
40 9502023 Splitting the Reference Time: Temporal Anaphora and Quantification in DRT (EACL95),
R. Nelken, N. Francez
41 9502024 A Robust Parser Based on Syntactic Information (EACL95), K. Lee et al.
42 9502031 Cooperative Error Handling and Shallow Processing (EACL95 Student), T. Bowden
43 9502033 An Algorithm to Co-ordinate Anaphora Resolution and PPS Disambiguation Process
(EACL95 Student), S. Azzam
44 9502035 Incorporating “Unconscious Reanalysis” into an Incremental, Monotonic Parser
(EACL95 Student), P. Sturt
45 9502037 A State-Transition Grammar for Data-Oriented Parsing (EACL95 Student), D. Tugwell
46 9502038 Implementation and Evaluation of a German HMM for POS Disambiguation (EACL95
Workshop), H. Feldweg
47 9502039 Multilingual Sentence Categorization According to Language (EACL95 Workshop),
E. Giguet
48 9503002 Computational Dialectology in Irish Gaelic (EACL95), B. Kessler
49 9503004 Creating a Tagset, Lexicon and Guesser for a French Tagger (EACL95 Workshop),
J. Chanod, P. Tapanainen
50 9503005 A Specification Language for Lexical Functional Grammars (EACL95), P. Blackburn,
C. Gardent
51 9503007 The Semantics of Motion (EACL95), P. Sablayrolles
52 9503009 Distributional Part-of-Speech Tagging (EACL95), H. Schuetze
53 9503013 Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Seman-
tics (COLING95), D. Milward, R. Cooper
54 9503014 Non-constituent Coordination: Theory and Practice (COLING94), D. Milward
55 9503015 Incremental Interpretation of Categorial Grammar (EACL95), D. Milward
56 9503017 Redundancy in Collaborative Dialogue (COLING92), M. Walker
57 9503018 Discourse and Deliberation: Testing a Collaborative Strategy (COLING94), M. Walker
58 9503023 A Fast Partial Parse of Natural Language Sentences Using a Connectionist Method
(EACL95), C. Lyon, B. Dickerson
59 9503025 Occurrence Vectors from Corpora vs. Distance Vectors from Dictionaries (COLING94),
Y. Niwa, Y. Nitta
60 9504002 Tagset Design and Inflected Languages (EACL95 Workshop), D. Elworthy
61 9504006 Cues and Control in Expert-Client Dialogues (ACL88), S. Whittaker, P. Stenton
62 9504007 Mixed Initiative in Dialogue: An Investigation into Discourse Segmentation (ACL90),
M. Walker, S. Whittaker
63 9504017 A Uniform Treatment of Pragmatic Inferences in Simple and Complex Utterances and
Sequences of Utterances (ACL95), D. Marcu, G. Hirst
64 9504024 A Morphographemic Model for Error Correction in Nonconcatenative Strings (ACL95),
T. Bowden, G. Kiraz
65 9504026 The Intersection of Finite State Automata and Definite Clause Grammars (ACL95),
G. van Noord
66 9504027 An Efficient Generation Algorithm for Lexicalist MT (ACL95), V. Poznanski et al.
67 9504030 Statistical Decision-Tree Models for Parsing (ACL95), D. Magerman
68 9504033 Corpus Statistics Meet the Noun Compound: Some Empirical Results (ACL95), M. Lauer
79 9504034 Bayesian Grammar Induction for Language Modeling (ACL95), S. Chen
70 9505001 Response Generation in Collaborative Negotiation (ACL95), J. Chu-Carroll, S. Carberry
71 9506004 Using Higher-Order Logic Programming for Semantic Interpretation of Coordinate Con-
structs (ACL95), S. Kulick
72 9511001 Countability and Number in Japanese-to-English Machine Translation (COLING94),
F. Bond et al.
442
Computational Linguistics Volume 28, Number 4
73 9511006 Disambiguating Noun Groupings with Respect to WordNet Senses (ACL95 Workshop),
P. Resnik
74 9601004 Similarity between Words Computed by Spreading Activation on an English Dictionary
(EACL93), H. Kozima, T. Furugori
75 9604019 Magic for Filter Optimization in Dynamic Bottom-up Processing (ACL96), G. Minnen
76 9604022 Unsupervised Learning of Word-Category Guessing Rules (ACL96), A. Mikheev
77 9605013 Learning Dependencies between Case Frame Slots (COLING96), H. Li, N. Abe
78 9605014 Clustering Words with the MDL Principle (COLING96), H. Li, N. Abe
79 9605016 Parsing for Semidirectional Lambek Grammar is NP-Complete (ACL96), J. Doerre
Acknowledgments
The work reported in this article was
conducted while both authors were in the
HCRC Language Technology Group at the
University of Edinburgh.
The authors would like to thank Jean
Carletta for her help with the experimental
design, Chris Brew for many helpful
discussions, Claire Grover and Andrei
Mikheev for advice on the XML
implementation, and the annotators, Vasilis
Karaiskos and Anne Wilson, for their
meticulous work and criticism, which led to
several improvements in the annotation
scheme. Thanks also to Byron
Georgantopolous, who helped to collect the
first version of the corpus, and to the four
anonymous reviewers.

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