Spoken and Written News Story Segmentation using Lexical Chains 
 Nicola Stokes. 
Department of Computer Science, 
University College Dublin, Ireland. 
Nicola.Stokes@ucd.ie 
 
 
 
Abstract 
In this paper we describe a novel approach to 
lexical chain based segmentation of broadcast 
news stories. Our segmentation system        
SeLeCT is evaluated with respect to two other 
lexical cohesion based segmenters TextTiling 
and C99. Using the Pk and WindowDiff 
evaluation metrics we show that SeLeCT   
outperforms both systems on spoken news 
transcripts (CNN) while the C99 algorithm 
performs best on the written newswire        
collection (Reuters). We also examine the   
differences between spoken and written news 
styles and how these differences can affect 
segmentation accuracy. 
1 Introduction 
Text segmentation can be defined as the automatic iden-
tification of boundaries between distinct textual units 
(segments) in a textual document. The aim of early 
segmentation research was to model the discourse struc-
ture of a text, thus focusing on the detection of fine-
grained topic shifts, at a clausal, sentence or pas-
sage/subtopic level (Hearst 1997). More recently with 
the introduction of the TDT initiative (Allan et al. 1998) 
segmentation research has concentrated on the detection 
of coarse-grained topic shifts resulting in the identifica-
tion of story boundaries in news feeds. In particular, un-
segmented broadcast news streams represent a challeng-
ing real-world application for text segmentation ap-
proaches, since the success of other tasks such as topic 
tracking or first story detection depend heavily on the 
correct identification of distinct and non-overlapping 
news stories. Most approaches to story segmentation use 
either Information Extraction techniques (cue phrase 
extraction), techniques based on lexical cohesion analy-
sis or a combination of both (Reynar 1998; Beeferman 
et al. 1999). More recently promising results have also 
been achieved though the use of Hidden Markov model-
ing techniques, which are commonly used in speech 
recognition applications (Mulbregt et al. 1999). 
      In this paper we focus on lexical cohesion based 
approaches to story segmentation. Lexical cohesion is 
one element of a broader linguistic device called cohe-
sion which is describe as the textual quality responsible 
for making the elements of a text appear unified or con-
nected. More specifically, lexical cohesion ‘is the cohe-
sion that arises from semantic relationships between 
words’ (Morris, Hirst 1991).  With respect to segmenta-
tion, an analysis of lexical cohesion can be used to indi-
cate portions of text that represent single topical units or 
segments i.e. they contain a high number of semanti-
cally related words. Almost all approaches to lexical 
cohesion based segmentation examine patterns of syn-
tactic repetition in the text e.g. (Reynar 1998; Hearst 
1997; Choi 2000). However, there are four additional 
types of lexical cohesion present in text: synonymy 
(car, automobile), specialization/generalization (horse, 
stallion), part-whole/whole-part (politicians, govern-
ment) and statistical co-occurrences (Osama bin Laden, 
World Trade Center). Lexical chaining based ap-
proaches to text segmentation, on the other hand, ana-
lyse all aspects of lexical cohesion in text. Lexical 
chains are defined as groups of semantically related 
words that represent the lexical cohesive structure of a 
text e.g. {flower, petal, rose, garden, tree}. In our lexi-
cal chaining implementation, words are clustered based 
on the existence of statistical relationships and lexico-
graphical associations (provided by the WordNet online 
thesaurus) between terms in a text. 
      There have been three previous attempts to tackle 
text segmentation using lexical chains. The first by 
Okumara and Honda (1994) involved an evaluation 
based on five Japanese texts, the second by Stairmand 
(1997) used twelve general interest magazine articles 
and the third by Kan et al. (1998) used fifteen Wall 
Street Journal and five Economist articles. All of these 
attempts focus on sub-topic rather than story segmenta-
tion. In contrast, this paper investigates the usefulness of 
lexical chains as a technique for determining story seg-
ments in spoken and written broadcast news streams. In 
Section 2, we explain how this technique can be refined 
                                                               Edmonton, May-June 2003
                                                 Student Research Workshop , pp. 49-54
                                                         Proceedings of HLT-NAACL 2003
to address story segmentation. In Section 3, we compare 
the segmentation performance of our lexical chaining 
algorithm with two other well known lexical cohesion 
based approaches to segmentation; namely TextTiling 
(Hearst 1997) and C99 (Choi 2000). Finally we examine 
the grammatical differences between written and spoken 
news media and show how these differences can be util-
ized to improve spoken transcript segmentation accu-
racy.  
2 SeLeCT: Segmentation using Lexical 
Chains on Text  
In this section we present our topic segmenter SeLeCT. 
This system takes a concatenated stream of text and 
returns a segmented stream of distinct news reports. The 
system consists of three components a ‘Tokeniser’, a 
‘Chainer’ which creates lexical chains, and a ‘Detector’ 
that uses these chains to determine news story bounda-
ries. More detailed descriptions of the ‘Tokeniser’ and 
‘Chainer’ components are reported in Stokes et al. 
(2003).  
2.1  The Tokeniser 
The objective of the chain formation process is to build 
a set of lexical chains that capture the cohesive structure 
of the input stream. Before work can begin on lexical 
chain identification, each sample text is processed by a 
part-of-speech tagger. Morphological analysis is then 
performed on these tagged texts; all plural nouns are 
transformed into their singular form, adjectives pertain-
ing to nouns are nominalized and all sequences of words 
that match grammatical structures of compound noun 
phrases are extracted. This idea is based on a simple 
heuristic proposed by Justeson and Katz (Justeson, Katz 
1995), which involves scanning part-of-speech tagged 
texts for patterns of adjacent tags that commonly match 
proper noun phrases like ‘White House aid’, ‘PLO 
leader Yasir Arafat’, and WordNet noun phrases like 
‘red wine’ or  ‘act of god’. Since the likelihood of find-
ing exact syntactic matches of these phrases elsewhere 
in a story is low, we include a fuzzy string matching 
function in the lexical chainer to identify related phrases 
like George_Bush  President_Bush.  
2.2 The Lexical Chainer 
The aim of the Chainer is to find relationships between 
tokens (nouns, proper nouns, compound nouns, nomi-
nalized adjectives) in the data set using the WordNet 
thesaurus and a set of statistical word associations, and 
to then create lexical chains from these relationships 
with respect to a set of chain membership rules. The 
chaining procedure is based on a single-pass clustering 
algorithm, where the first token in the input stream be-
comes the head of the first lexical chain. Each subse-
quent token is then added to the most recently updated 
chain that it shares the strongest semantic relationship1 
with. This process is continued until all tokens in the 
text have been chained. Our chaining algorithm is simi-
lar to one proposed by St Onge (1995) for the detection 
of malapropisms in text, however statistical word asso-
ciations and proper nouns were not considered in his 
original implementation. 
2.3  Boundary Detection 
The final step in the segmentation process is to partition 
the text into its individual news stories based on the 
patterns of lexical cohesion identified by the Chainer in 
the previous step. Our boundary detection algorithm is a 
variation on one devised by Okumara and Honda 
(Okumara, Honda 1994) and is based on the following 
observation: 
‘Since lexical chain spans (i.e. start and end points) 
represent semantically related units in a text, a high 
concentration of chain begin and end points between 
two adjacent textual units is a good indication of a 
boundary point between two distinct news stories’ 
We define boundary strength w(n, n+1) between each 
pair of adjacent textual unit in our test set, as the sum of 
the number of lexical chains whose span ends at para-
graph n and the number of chains that begin their span 
at paragraph n+1. When all boundary strengths between 
adjacent paragraphs have been calculated we then get the 
mean of all the non-zero cohesive strength scores. This 
mean value then acts as the minimum allowable boundary 
strength that must be exceeded if the end of textual unit n 
is to be classified as the boundary point between two news 
stories.  
       Finally these boundary strength scores are ‘cleaned’ 
using an error reduction filter which removes all bound-
ary points which are separated by less than x number of 
textual units from a higher scoring boundary, where x is 
too small to be a ‘reasonable’ story length. This filter 
has the effect of smoothing out local maxima in the 
boundary score distribution, thus increasing segmenta-
tion precision. Different occurrences of this error are 
illustrated in Figure 1. Regions A and C represent clusters 
of adjacent boundary points. In this situation only the 
boundary with the highest score in the cluster is retained 
as the true story boundary. Therefore the boundary which 
scores 6 is retained in region A while in region C both 
points have the same score so in this case we consider the 
last point in region C to be the correct boundary position. 
Finally, the story boundary in region B is also eliminated 
because it is situated too close to the boundary points in 
                                                 
1 Repetition is the strongest cohesive relationship, followed by 
synonymy, and then statistical associations, generaliza-
tion/specialization and part-whole/whole-part relationships. 
C B A 
region C and it has a lower score than either of those 
boundaries.  
0 0
4
6 5
0 0 0 0 0 0 0
3
0 0 0
5 5
0 0
 
Figure 1. Diagram shows different types of segmentation 
error; numbers greater than zero are possible boundary 
positions, while zero scores represent no story boundary 
point between these two textual units. 
3 Segmentation Evaluation 
In this section we give details of two news story seg-
mentation test sets, some evaluation metrics used to 
determine segmentation accuracy, and the performance 
results of the SeLeCT, C99 and TextTiling algorithms. 
3.1 News Segmentation Test Collections  
Both the CNN and Reuters test collections referred to in 
this paper contain 1000 randomly selected news stories 
taken from the TDT1 corpus. These test collections 
were then reorganized into 40 files each consisting of 25 
concatenated news stories. Consequently, all 
experimental results in Section 3.3 are averaged scores 
generated from the individual results calculated for each 
of the 40 samples. By definition a segment in this 
context refers to a distinct news story, thus eliminating 
the need for a set of human-judged topic shifts for 
assessing system accuracy. 
3.2 Evaluation Metrics 
There has been much debate in the segmentation litera-
ture regarding appropriate evaluation metrics for esti-
mating segmentation accuracy.  Earlier experiments 
favored an IR style evaluation that measures perform-
ance in terms of recall and precision. However these 
metrics were deemed insufficiently sensitive when try-
ing to determine system parameters that yield optimal 
performance. The most widely used evaluation metric is 
Beeferman et al.’s (1999) probabilistic error metric 
Pk, which calculates segmentation accuracy with respect 
to three different types of segmentation error: false posi-
tives (falsely detected segments), false negatives 
(missed segments) and near-misses (very close but not 
exact boundaries). However, in a recent publication 
Pevzner and Hearst (2002) highlight several faults with 
the Pk metric. Most notable they criticize Pk for its un-
fair penalization of false negatives over false positives 
and its over-penalization of near-misses. In their paper, 
the authors proposed an alternative error metric called 
WindowDiff which rectifies these problems. 
3.3 Story Segmentation Results 
In this section we present performance results for each 
segmenter on both the CNN and Reuters test sets with 
respect to the aforementioned evaluation metrics. As 
explained in Section 3, we determine the effectiveness 
of our SeLeCT system with respect to two other lexical 
cohesion based approaches to segmentation, namely the 
TextTiling (Hearst 1997) and C99 algorithms (Choi 
2000)2. We also include average results from a random 
segmenter that returned 25 random boundary positions 
for each of the 40 files in both test sets. These results 
represent a lower bound on segmentation performance. 
All results in this section are calculated using para-
graphs as the basic unit of text. Since both our test sets 
are in SGML format, we consider the beginning of a 
paragraph in this context to be indicated by a speaker 
change tag in the CNN transcripts and a paragraph tag 
in the case of the Reuters news stories.  
 Table 1: Pk and WD (WindowDiff) values for segmenta-
tion systems on CNN and Reuters Collections. 
Table 1 summarizes the results of the CNN data set 
for each segmentation system evaluated with respect to 
the four metrics. All values for these metrics range from 
0 to 1 inclusively, where 0 represents the lowest possi-
ble measure of system error. From these results we ob-
serve that the accuracy of our SeLeCT segmentation 
algorithm is greater than the accuracy of C99, 
TextTiling or the Random segmenter for both evalua-
tion metrics on the CNN ‘spoken’ data set. As for the 
Reuters segmentation performance, the C99 algorithm 
significantly outperforms both the SeLeCT and 
TextTiling systems. We also observe that the Win-
dowDiff metric penalizes systems more than Pk, how-
ever the overall ranking of the systems with respect to 
these error metrics remains the same. With regard to the 
SeLeCT system, optimal performance was achieved 
when only patterns of lexical repetition were examined 
during the boundary detection phase, thus eliminating 
the need for an examination of lexicographical and sta-
tistical relationships between tokens in the text.            
                                                 
2 We use Choi’s java implementations of TextTiling and C99 
available for free download at www.cs.man.ac.uk/~choif. In 
(Choi 2000) boundaries are hypothesized using sentences as 
the basic unit of text; however both C99 and TextTiling can 
take advantage of paragraph information when the input con-
sists of one paragraph per line.  
CNN  Reuters  System 
Pk WD Pk WD 
SeLeCT 0.25 0.253 0.191 0.207 
TextTiling 0.259 0.299 0.221 0.244 
C99 0.294 0.351 0.128 0.148 
Random 0.421 0.48 0.490 0.514 
A similar conclusion was reported by Hearst (1997) and 
Min-Yen et al. (1998); however neither of these ap-
proaches included statistical word associations in their 
chaining process. 
4 Written and Spoken Text Segmentation 
It is evident from the results of our segmentation ex-
periments on the CNN and Reuters test collections that 
system performance is dependant on the type of news 
source being segmented i.e. spoken texts are more diffi-
cult to segment. This disagreement between result sets 
is a largely unsurprising outcome as it is well docu-
mented by the linguistic community that written and 
spoken language modes differ greatly in the way in 
which they convey information. At a first glance, it is 
obvious that written texts tend to use more formal and 
verbose language than their spoken equivalents. How-
ever, although CNN transcripts share certain spoken text 
characteristics (see Section 4.1), they lie somewhere 
nearer written documents on a spectrum of linguistic 
forms of expression, since they contain a mixture of 
speech styles ranging from formal prepared speeches 
from anchor people, politicians, and correspondents, to 
informal interviews/comments from ordinary members 
of the public. Furthermore, spoken language is also 
characterized by false starts, hesitations, back-trackings, 
and interjections; however information regarding pro-
sodic features and these characteristics are not repre-
sented in CNN transcripts. In the next section we look at 
some grammatical differences between spoken and writ-
ten text that are actually evident in CNN transcripts. In 
particular, we look at the effect that these differences 
have on parts of speech distributions and how these im-
pact segmentation performance. 
4.1 Lexical Density    
One method of measuring the grammatical intricacy of 
speech compared to written text, is to calculate the lexi-
cal density of the language being used. The simplest 
measure of lexical density, as defined by Halliday 
(1995), is the ‘the number of lexical items (content 
words) as a portion of the number of running words 
(grammatical words)’. Halliday states that written texts 
are more lexically dense while spoken texts are more 
lexically sparse. In accordance with this, we observe 
based on part-of-speech tag information that the CNN 
test set contains 8.58% less lexical items than the 
Reuters news collection.3 
                                                 
3 Lexical items included all nouns, adjectives and verbs, ex-
cept for function verbs like modals and auxiliary verbs. In-
stead these verbs form part of the grammatical item lexicon 
with all remaining parts of speech. Our CNN and Reuters data 
sets consisted of 43.68% and 52.26% lexical items respec-
tively.   
Halliday explains that this difference in lexical den-
sity between the two modes of expression can be attrib-
uted to the following observation: 
‘Written language represents phenomena as products, 
while spoken language represents phenomena as proc-
esses.’ 
In real terms this means that written text tends to con-
veys most of its meaning though nouns (NN) and adjec-
tives (ADJ), while spoken text conveys it though ad-
verbs (ADV) and verbs (VB). To illustrate this point 
consider the following written and spoken paraphrase of 
the same information:  
Written: Improvements/NN in American zoos 
have resulted in better living/ADJ conditions 
for their animal residents/NN. 
Spoken: Since/RB American zoos have been 
improved/VB the animals residing/VB in them 
are now/RB living/VB in better conditions. 
Although this example is a little contrived, it shows 
that in spite of changes to the grammar, by and large the 
vocabulary has remained the same. More specifically, 
these paraphrases illustrate how the products in the writ-
ten version, improvements, resident, and living, are con-
veyed as processes in spoken language though the use 
of verbs. The spoken variant also contains more ad-
verbs; a grammatical necessity that provides cohesion to 
text when processes are being described in verb clauses.  
As explained in Section 2.2 the SeLeCT lexical 
chainer only looks at cohesive relationships between 
nouns and nominalized adjectives in a text. This ac-
counts partly for SeLeCT’s lower performance on the 
CNN test set, since the extra information conveyed 
though verbs in spoken texts is ignored by the lexical 
chainer. However since C99 and TextTiling use all parts 
of speech in their analysis of the text, the replacement of 
products with processes is not the reason for a similar 
deterioration in their performance. More specifically, 
both C99 and TextTiling rely on stopword lists to iden-
tifying spurious inter-segment links between function 
words that by their nature do not indicate common topi-
cality. For the purpose of their original implementation 
their stopwords lists contained mostly pronouns, deter-
miners, adverbs, and function verbs such as auxiliary 
and modal verbs. However, we have observed that the 
standard set of textual function verbs is not enough for 
speech text processing tasks and that their lists should 
be extended to include other common ‘low information’ 
verbs. These types of verbs are not necessarily charac-
terized by large frequency counts in the spoken news 
collection like the domain specific phrases to report or 
to comment. Instead these verbs tend to have no 
‘equivalent’ nominal form, like the verbs ‘to let’ ‘to 
hear’ ‘to look’ or ‘to try’. 
To test this observation we re-ran C99 and 
TextTiling experiments on the Reuters and CNN      
C B A 
collections, using only nouns, adjectives, nominalized 
verbs (provided by the NOMLEX (Meyers et al. 1998)), 
and nominalized adjectives as input. Our results show 
that there is a significant decrease in WindowDiff error 
for the C99 system on both the CNN collection (a de-
crease from 0.351 to 0.268) and the Reuters collection 
(a decrease from 0.148 to 0.121). Similarly, we observe 
an improvement in the WindowDiff based performance 
of the TextTiling system on the CNN data set (a de-
crease from 0.299 to 0.274). However, we observe a 
marginal fall in performance on the Reuters data set (an 
increase from 0.244 to 0.247). These results illustrate 
the increased dominance of verbs in spoken text and the 
importance of function verb removal by our verb nomi-
nalization process for CNN segmentation performance.   
4.2 Reference and Conjunction in Spoken Text 
A picture paints a thousand words, they say, and since 
news programme transcripts are accompanied by visual 
and audio cues in the news stream, there will always be 
a loss in communicative value when transcripts are in-
terpreted independently. As stated in Section 4.1, it is 
well known that conversational speech is accompanied 
by prosodic and paralinguistic contributions, facial ex-
pressions, gestures, intonation etc., which are rarely 
conveyed in spoken transcripts. However there are also 
explicit (exophoric) references in the transcript to events 
occurring outside the lexical system itself. These exo-
phoric references in CNN transcripts relate specifically 
to audio references like speaker change, musical inter-
ludes, background noise; and visual references like 
event, location and people shots in the video stream.  
We believe that this property of transcribed news is an-
other reason for the deterioration in segmentation per-
formance on the CNN test collection. 
Solving endophoric (anaphora and cataphora) and 
exophoric reference has long been recognized as a very 
difficult problem, which requires pragmatic, semantic 
and syntactic knowledge in order to be solved. However 
there are simple heuristics commonly used by text seg-
mentation algorithms that in our case can be used to 
take advantage of the increased presence of reference in 
spoken text. One such heuristic is based on the observa-
tion that when common referents like personal and pos-
sessive pronouns, and possessive determiners appear at 
the beginning of a sentence, this indicates that these 
referents are linked in some way to the previous textual 
unit (in our case the previous paragraph). The resolution 
of these references is not of interest to our algorithm but 
the fact that two textual units are linked in this way 
gives the boundary detection process an added advan-
tage when determining story segments in the text. An 
analysis of conjunction (another form of textual cohe-
sion) can also be used to provide the detection process 
with useful evidence of related paragraphs, since para-
graphs that begin with conjunctions (because, and, or, 
however, nevertheless) and conjunctive phrases (in the 
mean time, in addition, on the other hand) are particu-
larly useful in identify cohesive links between units in 
conversational/interview sequences in the transcript. 
4.3 Refining SeLeCT Boundary Detection  
In Section 2.3 we describe in detail how the boundary 
detection phrase uses lexical chaining information to 
determine story segments in a text. One approach to 
integrating referential and conjunctive information with 
the lexical cohesion analysis provided by the chains is 
to remove all paragraphs from the system output that 
contain a reference or conjunctive relationship with the 
paragraph immediately following it in the text. The 
problem with this approach is that Pk and WindowDiff 
errors will increase if ‘incorrect’ segment end points are 
removed that represented near system misses rather than 
‘pure’ false positives. Hence, we take a more measured 
approach to integration that uses conjunctive and refer-
ential evidence in the final filtering step of the detection 
phrase, to eliminate boundaries in boundary clusters 
(Section 2.3) that cannot be story end points in the news 
stream.  Figure 2 illustrates how this technique can be 
used to refine the filtering step. Originally, the boundary 
with score six in region A would have been considered 
the correct boundary point. However since a conjunctive 
phrase links the adjacent paragraphs at this boundary 
position in the text, the boundary which scores five is 
deemed the correct boundary point by the algorithm. 
0 0
4
6 5
0 0 0 0 0 0 0
3
0 0 0
5 5
0 0
 
Figure 2 Illustrates how cohesion information can help 
SeLeCT’s boundary detector resolve clusters of possible 
story boundaries. 
Using this technique and the verb nominalization proc-
ess described in section 4.1 on both news media collec-
tions, we observed an improvement in SeLeCT system 
performance on the CNN data set (a decrease in error 
from 0.253 to 0.225), but no such improvement on the 
Reuters collection. Again the ineffectiveness of this 
technique on the Reuters results can be attributed to 
differences between the two modes of language expres-
sion, where conjunctive and referential relationships 
resolve 51.66% of the total possible set of boundary 
points between stories in the CNN collection and only 
22.04% in the Reuters collection. In addition, these ref-
erences in the Reuters articles mostly occur between 
sentences in a paragraph rather than between paragraphs 
in the text thus provide no additional cohesive          
information. A summary of the improved results dis-
cussed in this section is shown in Table 2. 
CNN WD Score Reuters WD Score System 
Before After Before After 
 SeLeCT 0.253 0.225 0.207 0.209 
 C99 0.351 0.268 0.148 0.121 
 TextTiling 0.299 0.274 0.244 0.247 
Table 2: Improvements in system performance as a result 
of system modifications discuss in Sections 4.1 and 4.3. 
5 Conclusions 
In this paper we have presented a lexical chaining based 
approach to coarse-grained segmentation of CNN news 
transcripts and concatenated Reuters newswire articles. 
We have shown that the performance of our SeLeCT 
system exceeds that of the TextTiling and C99 systems 
when detecting topic shifts in CNN transcripts. How-
ever the results of a similar experiment on Reuters news 
stories showed that the C99 system outperformed all 
other systems on a written news collection. Overall, 
lower CNN segmentation results were attributed to the 
information loss caused by prosodic and paralinguistic 
characteristics of speech and grammatical differences 
between written and spoken modes of expression. Fur-
ther experiments showed that by limiting the input of all 
the segmentation systems to nouns, adjectives, and 
nominalized verbs and adjectives, the effect of these 
grammatical differences on CNN segmentation per-
formance was significantly reduced. Additional SeLeCT 
performance improvements were also achieved by using 
referential and conjunctive relationships as additional 
evidence of cohesion in the boundary detection step. In 
future experiments we plan to compare SeLeCT’s per-
formance on written and spoken news texts with two 
recently proposed systems, U00 (Utiyama 2001) and 
CWM (Choi 2001), which have marginally outper-
formed the C99 algorithm on Choi’s (2000) test corpus. 
Acknowledgements  
The support of Enterprise Ireland is gratefully acknowledged. 
Also I wish to thank Marti Hearst for providing us with a ver-
sion of the WindowDiff evaluation software and Joe Carthy for 
invaluable comments. 
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