A RULE-BASED APPROACH TO EVALUATING IMPORTANCE IN DESCRIPTIVE TEXTS 
Danilo Fum(*), Giovanni Gulda(?), Carlo Tasso(~) 
Isllmm di Matemadca, Informatica • Slstemistica 
Universi~ di Udlne 
Udlne, Italy 
ABSTRACT 
Importance evaluation is one of the most 
challenging problems in the field of text 
processing. In the paper we focus on the notion of 
importance from a computational standpoint, and we 
propose a procedural, rule-based approach to 
importance evaluation. This novel approach is 
supported by a prototype experimental system, 
called importance evaluator, that can deal with 
descriptive texts taken from computer science 
literature on operating systems. The evaluator 
relies on a set of importance rules that are used 
to assign importance values to the different parts 
of a text and to resolve or explain conflicting 
evaluations. The system utilizes world knowledge 
on the subject domain contained in an encyclopedia 
and takes into account a goal assigned by the user 
for specifying the pragmatic aspects of the 
understanding activity. The paper describes the 
role of the evaluator in the frame of a larger 
system for text summarization (SUSY); it 
illustrates its overall mode of operation, and 
discusses some meaningful examples. 
I. INTRODUCTION 
Text understanding has received increasing 
attention in recent years. A major problem in this 
area is that of importance evaluation: not all the 
components of a sufficiently large and structured 
piece of text are equally important for the reader, 
and humans are able to evaluate the relative 
importance of the parts of the texts they read. 
This issue has been faced so far only in an 
indirect way in the literature on discourse 
structure (Kintsch and van Dijk, 1978; van Dijk 
and Kintsch, 1983), summarization (Lehrert, 1982 
and 1984; Wilensky, 1982; Hahn and Reimer, 1984), 
and inference (Schank, 1979). 
Moreover, several studies in the field of 
summarization (e.g.: Schank, 1979 and 1982; 
Lehnert, 1982 and 1984; Wilensky, 1982) have 
mostly been concerned with narrative texts 
(stories), and it is not at all obvious that the 
approaches which proved successful in this area 
(') also with: Laboratorio di Psicologia E.E., 
Universita' di Trieste, Trieste, Italy 
(t) also with: Milan Polytechnic Artificial 
Intelligence Project, Milano, Italy 
(~) also with: CISM International Center for 
Mechanical Sciences, Udine, Italy 
could be applied to descriptive texts as well. 
Expository prose has its own specific features 
(Graesser, 1981), and it seems to require different 
understanding processes, different summarization 
skills, and different cognitive models (Lehnert, 
1984). Work on the problem of understanding and 
summarizing expository prose is still at the very 
beginning (Hahn and Reimer, 1984). 
In this paper we focus on the notion of 
importance from a computational standpoint, and we 
propose a rule-based approach to importance 
evaluation. This research is part of a larger 
project aimed at developi ng a system for 
understanding and summarizing descriptive texts 
(SUSY, a SUmmarizing SYstem), which is in progress 
a~-~Te University" of 
SUSY proposes an approach to descriptive text 
understanding and summarization (Fum, Guida, and 
Tasso, 1982, 1983, and 1984) in which the process 
of representing the meaning of a natural language 
text is split into three main tasks, namely: 
sentence understanding, structure capturing, and 
i mportance eval uati on. 
The sentence understandin 9 phase works on the 
single s~ that constitute a given natural 
language text and maps them into a formal internal 
representation, called basic linear representation 
(BLR). The BLR is essentially a propositional 
language appropriately extended and completed to 
deal with the most relevant features of text 
representation, and fully worked out in a way 
suitable for computer implementation (Fum, Guida, 
and Tasso, 1984). The BLR representation of a text 
i s consti tuted by a sequence of I abel ed 
propositions, each of them constituted by a 
predicate with instantiated arguments or 
representing an ISA relation between concepts. 
This phase includes the understanding of the 
literal meaning of each sentence in the text, the 
appropriate representation of time relations, and 
the treatment of quantification and reference. 
The structure capturing phase works on the BLR 
and produces an augmented version of it, called 
extended linear representation (ELR). This phase 
on two maln points: 
- inferring and expliciting the 
macro-structure of the text (van Dijk, 
1977; Kintsch and van Dijk, 1978; van 
Dijk and Kintsch, 1983), that accounts for 
the conceptual connection (coherence) 
among sentences (Hobbs, 1982 and 1983); 
244 
recognizing and expliciting the rhetoric 
structure of the text, which explains how 
the flow of ideas and the arguments of the 
writer are organized and implemented in 
the text. 
The importance evaluation phase operates on 
the ELR and attaches appropriate markers to its 
components in order to produce a new 
representation, called hierarchical propositional 
network (HPN). The HPN is a tree-like structure 
whose n~s, corresponding to concepts and 
propositions of the ELR, are assigned different 
importance values (integers) according to their 
relative importance in the text. 
Once the HPN representation of a text has been 
produced, it is easy to prune the less relevant 
parts in order to obtain the representation of an 
appropriate summary to be eventually translated 
into natural language. These last phases (i.e., 
pruning and generation) are, for the moment, 
outside the scope of this research. 
The purpose of this paper is to investigate in 
some detail the phase of importance evaluation, and 
to illustrate the results obtained in the design 
and experimentation of a prototype system that can 
produce from the ELR of a given text a reasonable 
HPN. 
The paper is organized as follows: section two 
introduces the topic of importance evaluation and 
discusses some basic conceptual aspects, in section 
three the overall organization of the system is 
presented with particular attention to knowledge 
representation, section four illustrates some 
examples of importance evaluation, and section five 
concludes the paper. 
2. EVALUATING IMPORTANCE 
The topic of importance evaluation has been 
dealt with in recent years, although often only in 
a quite indirect way, by several authors and in 
many different contexts. A part of a text can be 
considered important in relation to other segments 
of the same text according to several criteria: 
it embodies knowledge necessary to 
understand other parts of the text (van 
Dijk, 1977; Kintsch and van Dijk, 1978); 
- it is relevant to the topic of discourse 
(Lehnert 1982 and 1984); 
it is useful to clarify the relations that 
make discourse coherent (Hobbs, 1982); 
it relates to the topic-focus articulation 
(Haji~ova' and Sgall, 1984); 
it refers to objects or relations in the 
subject domain that are judged to be 
important a-priori (Schank, 1979); 
- it is unusual, new, or abnormal in the 
subject domain (Schank, 1979); 
it generates surprise (van Dijk and 
Kintsch, 1983); 
it is relevant to some specific reader's 
goal or need (Fum, Guida, and Tasso, 
1982). 
In practice, if we test these criteria on 
sample texts, they result sometimes complementary, 
sometimes partially overlapping, sometimes even 
conflicting. Moreover, different readers may judge 
differently the importance of the same text; on 
some parts a general consensus may be achieved, but 
the evaluation of other parts may be definitely 
subjective. 
In fact, "important" means "specially relevant to 
some goal", and, whenever the goal with which a 
text is read changes, the parts of text which are 
to be considered important vary accordingly. Even 
if the goal of reading is only seldom considered 
explicitly by humans, still some goal is always 
implicitly assumed. Different readers (or the same 
reader in different moments) may have different 
goals, and conflicting judgments of importance may 
be due to the consideration of different goals, 
rather than to the application of different 
evaluation procedures. 
The above investigation shows that importance 
is a really multifaceted concept which escapes a 
simple, explicit, algorithmic definition. A 
procedural, knowledge-based approach comprising a 
set of rules that can assign relative importance 
values to the different parts of a text and can 
resolve or explain conflicting evaluations seems 
more appropriate. Such an approach allows taking 
into account in a flexible and natural way the 
variety of knowledge sources and processing 
activities that are involved in importance 
evaluation. Moreover, it is expected to be well 
founded from a cognitive point of view (van Dijk 
and Kintsch, 1983; Anderson, 1976), as it allows 
close and transparent modeling of several processes 
that occur in human mind. 
3. A COMPUTATIONAL APPROACH 
Most of the ideas outlined in the previous 
section have been implemented in the design of a 
subsystem of SUSY, called the importance evaluator, 
that takes in input the ELR representation of a 
natural language text and the representation of a 
reader's goal and produces in output the 
corresponding HPN. The evaluator is implemented by 
a rule-based system (Davis and King, 1976) with a 
forward chaining control regime. Knowledge 
available to the evaluator comprises two parts: a 
rule base and an encyclopedia. 
The rule base embodies expert knowledge 
necessary ~r importance evaluation. It is 
constituted by production rules, called importance 
rules, having the usual IF-THEN form. Rules can be 
245 
classified according to their competence, i.e. to 
the different types of knowledge utilized for 
evaluating importance. From this point of view, 
three classes of rules are considered: 
structural rules, which express the fact 
that some parts of the text can be judged 
important just by looking at their 
structure and organization, discarding 
thei r meaning; 
semantic ~ules, which can evaluate 
importance by specifically taking into 
account some specific structural features 
of the text that convey a definite 
meaning; 
encyclopedic rules, which can evaluate 
importance by comparing the meaning of the 
text with domain specific knowledge 
contained in the encyclopedia. 
The IF-part of the rules contains conditions that 
are evaluated with respect to the current HPN 
(initially the ELR), and the THEN-part specifies 
either an importance evaluation or an action to be 
performed to further the analysis (e.g., a 
strategic choice, a criterion to solve conflicting 
evaluations, etc.). 
The evaluation of importance contained in the 
THEN-part of a rule takes usually the form of an 
ordering relation (e.g., less, equal, etc.) among 
importance values of concepts or propositions of 
the ELR, or it specifies ranges of importance 
values (e.g., high, low, etc.). Thus, rules only 
assert relative importance of different parts of 
the text: a constraint propagation algorithm will 
eventually transform these relative evaluations 
into absolute importance values according to a 
given scale. 
The encyclopedia is the second knowledge 
source employed by the evaluator and it contains 
domain specific knowledge. Encyclopedic knowledge 
is represented through a net of frames. Frames 
embody, in addition to a header, two kinds of 
slots: 
knowledge slots, that contain domain 
specific knowledge, represented in a form 
homogeneous with the propositional 
language of the ELR; 
reference slots, containing pointers to 
other frames that deal with related topics 
in the subject domain. 
This organization allows easy implementation of a 
property inheritance mechanism. 
We now illustrate the notion of goal which is 
of crucial importance for understanding the overall 
mode of operation of the evaluator. The goal is a 
chunk of variable knowledge, assigned by the user 
taking into account the pragmatic aspects of the 
understanding activity, that defines the 
motivations and objectives that are behind the 
reading process. The role of the goal is twofold: 
exerting control on the activation of 
importance rules that operate on the ELR; 
selective focusing, i.e. enabling the 
evaluator to choose from the encyclopedia 
the pieces of knowledge which are expected 
to be relevant to the current importance 
evaluation. 
The use of the goal in selective focusing comprises 
two activities: 
validating matching between the current 
ELR and the knowledge contained in a frame 
header or knowledge slot (direct frame 
activation), or 
activating a new frame pointed at in a 
reference slot of a currently active frame 
(i ndi rect frame acti vati on ). 
Therefore, the encyclopedia does not contain any 
a-priory judgment about importance. Full 
responsibility of this activity is left to the 
evaluator, which can interpret the content of the 
encyclopedia frames according to the current goal 
and can use the extracted knowledge to support the 
rule-based evaluation process. 
4. SAMPLE OPERATION OF THE EVALUATOR 
The current prototype version of the evaluator 
operates on simple texts taken from scientific and 
technical computer science literature on operating 
systems. It includes about 40 importance rules and 
a small encyclopedia of about 30 frames. The goal 
has been assigned a very simple structure: it is a 
logical con~)ination of key-terms, chosen in a 
predefinite set, that represent possible points of 
view a reader can take in analyzing a text. 
In this section we will illustrate some of the 
most basic mechanisms of importance evaluation 
through a few examples. 
Let us consider the following sample text: 
"U-DOS is an operating system developed by 
Softproducts Ltd. in 1982. It has a modular 
organization and is suitable for real-time 
applications. U-DOS includes powerful tools for 
interactive processing and supports a sophisticated 
window management that makes it user friendly, i.e. 
easily usable by novices or untrained end-users, 
Easy operation is, in fact, the main reason of it 
widespread diffusion in the data processing market, 
especially among CAD/CAM users who appreciate its 
graphic utilities." 
The ELR of this text results (for 
description of the formalism refer to: 
and Tasso, 1984): 
a com~olete 
Fum, Guida, 
010 *OP-SYSTEM (U-DOS) 
020 DEVELOP (SOFTPRODUCTS-LTD, U-DOS, T1) 
246 
030 *PAST (T1) 
040 *YEAR-1982 (T1) 
045 TIME-SPEC (40,20) 
050 HAVE (U-DOS, V1, P) 
060 *ORGANIZATION (VI) 
070 MODULAR (V1, P) 
075 QUAL (70, 60) 
080 SUIT (U-DOS, VV2, P) 
090 *APPLICATION (VV2) 
100 REAL-TIME (VV2, P) 
105 QUAL (100, 90) 
110 INCLUDE (U-DOS, VV3, P) 
120 *TOOL (VV3) 
130 POWERFUL (VV3, P) 
135 QUAL (130, 120) 
140 APPROPRIATE-TO (VV3, V4, P) 
145 QUAL (140, 120) 
150 *PROCESSING (V4) 
160 INTERACTIVE (V4, P) 
165 QUAL (160, 150) 
170 SUPPORT (U-DOS, V5, P) 
180 *WINDOW-MANAGEMENT (V5) 
190 SOPHISTICATED (VS, P) 
195 QUAL (190, 180) 
200 MAKE (VS, U-DOS, 210, P) 
205 ENABLE (190, 210) 
210 USER-FRIENDLY (U-DOS, P) 
215 CLARIFICATION (220, 210) 
220 OR (230, 260, P) 
230 EASILY (240, P) 
240 USE (VV6, U-DOS, P) 
245 MOD (230, 240) 
250 *NOVICE (VV6) 
260 EASILY (270, P) 
270 USE (VV7, U-DOS, P) 
275 MOD (260, 270) 
280 *END-USER (VV7) 
290 UNTRAINED (VV7, P) 
295 QUAL (290, 280) 
300 REASON-FOR (310, 340) 
305 RESULT (310, 340) 
310 EASILY (320, P) 
320 OPERATE (NIL, U-DOS) 
325 MOD (310, 320) 
330 HAVE (U-DOS, V8, P) 
340 *DIFFUSION (V8) 
350 LARGE (VS, P) 
355 QUAL (350, 340) 
360 IN (330, vg, P) 
370 *DATA-PROCESSING-MARKET (V9) 
380 AMONG (330, VVIO, P) 
385 SPECIFICATION (360, 380) 
390 *CAD/CAM-USER (VVlO) 
400 APPRECIATE (VVlO, VV11, P) 
410 *UTILITY (VV11) 
420 GRAPHIC (VV11, P) 
425 QUAL (420, 410) 
430 HAVE (U-DOS, VV11, P) 
The set of key-terms that can be used to 
specify the goal includes, among others: KNOW, 
BUY, and USE. We assume hereinafter the goal KNOW, 
i.e., we are particularly interested in knowing the 
main technical features of the U-DOS operating 
system. With such a goal, some pieces of the 
encyclopedia turn out to be relevant to the 
evaluation of our sample text, while others are 
discarded, as it will be illustrated below. 
In order to analyze the text, the evaluator 
generates from the ELR, as a preliminary step, a 
new structure, called the cohesion graph, that 
explicitly shows all the references among 
propositions of the ELR. The cohesion graph is a 
bipartite graph whose nodes are constituted by 
concepts and propositions connected by three kinds 
of arcs: 
directed arcs connecting pairs of 
propositions (say from P to Q), which 
represent embedding of a proposition into 
another (Q in P); 
simple arcs, connecting a concept and a 
proposition, which indicate that the 
concept appears as an argument in the 
proposition; 
double directed arcs, connecting two 
concepts via a propositional node (say 
from A to B via P), which show that a 
concept enters as the argument of a 
proposition stating an ISA relation (P 
states that A ISA B). 
A portion of the cohesion graph of our sample text 
is shown in Figure 1. 
Structural rules can exploit the information 
provided by the cohesion graph in order to 
selectively capturing the importance of the 
different parts of the text. An example of a 
structural rule is: 
Rule $4: Highly Referenced Concept 
IF in the cohesion graph there is a concept C 
which is at least K-referenced 
THEN assign C an importance value w(C) = high. 
This rule guesses that a concept which is 
highly referenced in a text is probably important. 
In our example (where the parameter K is set equal 
to 3), the concept U-DOS is considered important as 
it is highly referenced. 
Importance can be evaluated by chaining 
several rules. As an example, after rule $4 has 
been applied, the following rule can fire: 
Rule MT: ISA Proposition 
IF a proposition P represents an ISA relation 
AND 
the argument of P is a concept C with 
importance value w(C) 
THEN assign P an importance value w(P) = w(C). 
The rationale of this rule is that, if a 
concept is important, any proposition that states 
an ISA relation about that concept is important 
too. This allows, for example, considering 
proposition 10 (which states that U-DOS is an 
operating system) as important. 
Rule M7 allows, moreover, the application 
247 
J SOFTPRODUCTS - LTO IORGANIZATIONI I J YEAR-1982 I 
APPLICATION 
( 
F 
U-DOS 
OP-SYSTEM J 
~ PROCESSING I 
WINDOW-MANAGEMENT I 
Fig. I: (Partial) Cohesion Graph of the Sample Text 
of the following rule: 
Rule E6: ISA Frame Activation 
IF a proposition P represents an ISA relation 
AND 
P has importance value w(P) > low 
AND 
the predicate of P is the header of a frame 
F in the encyclopedia 
THEN activate F. 
In our example, the fact that proposition 10 
is important (w(P) = high) and that it represents 
an ISA relation allows the OPERATING-SYSTEM frame 
to be activated (see Figure 2, where a portion of 
the encyclopedia relevant to the current example is 
shown). Note that rule E6 does not directly state 
whether a proposition or a concept has to be 
considered important or not, but it specifies which 
frames are to be considered relevant in the current 
context. 
Most evaluations are goal dependent and rely 
on a goal interpreter, able to evaluate a specific 
piece of ELR or a frame slot of the encyclopedia in 
order to determine its relevance to the current 
goal. The goal interpreter performs in such a way 
a complex matching, which allows implementation of 
selective focusing. Consider, for example, the 
following rule: 
RULE E19: Goal-Dependent Frame Activation 
IF the current goal matches a reference slot 
• R of an active frame 
THEN activate the frame whose header is pointed 
at by R. 
Successive applications of this rule allow 
activation, starting from the OPERATING-SYSTEM 
frame, of the SOFTWARE-SYSTEM frame and, then, of 
the COMPLEX-SYSTEM frame (see Figure 2). At this 
point the following rule applies: 
248 
OP-SYSTEM 
isa : SOFTWARE-SYSTEM 
PRODUCT 
has parts : KERNEL 
SCHEDULER 
MEMORY ALLOCATOR 
includes : OS/I 
RTOS 
XENIX 
basic operating characteristics: 
OP-SYSTEM (X) BATCH (X) 
REAL-TIME (X) 
MULTI-USER (X) 
OP-SYSTEM (X) RUN-ON (X,Y) 
COMPUTER (Y) FOR (X,Y) 
/ SOFTWARE-SYSTEM 
isa : COMPLEX-SYSTEM 
PROGRAM 
PROCEDURE 
has parts : MODULE 
ROUTINE 
SUBSYSTEM 
language : 
lO SOFTWARE-SYSTEM (X) 
20 LANGUAGE (Y) 
OF (lO,20) 
/ COMPLEX-SYSTEM 
structure : 
COMPLEX-SYSTEM (X) MODULAR (X) 
EXTENSIBLE (X) 
HIERARCHICAL (X) 
Fig. 2: Some Frames of the Encyclopedia 
Rule E25: Goal Dependent Matching 
IF a proposition P matches a pattern contained 
in a knowledge slot K of an active frame 
AND 
the current goal matches K 
THEN assign P an importance value w(P) = high. 
In our example, since (i) the COMPLEX-SYSTEM 
frame is active and proposition 70 of the ELR 
matches the pattern MODULAR (ORGANIZATION) of the 
"structure" slot of the frame, and (ii) the goal 
interpreter evaluates that the knowledge slot 
"structure" is relevant to the goal KNOW, then 
proposition 70 is considered important. 
As a last example, we illustrate a rule that 
exploits knowledge concerning the macro-structure 
of the text: 
Rule M9: Macro Clarification 
IF if there exists a macro-proposition 
CLARIFICATION (P, Q) 
THEN assign P and Q importance values such that 
w(P) < w(Q). 
Rule M9 implements the idea that a proposition 
which is used to clarify another proposition (i.e., 
it paraphrases its content or explains the meaning 
of some of its terms) has to be considered less 
important than the proposition it clarifies. This 
rule can be applied, for example, in rating 
propositions 210 and 220, the latter resulting less 
important than the former. 
5. CONCLUSION 
The importance evaluator described in the 
previous sections is written in Franz Lisp and it 
is presently running in a prototype version on a 
SUN-2 workstation. Much experimental work is 
currently ongoing on this prototype in order to 
assess its operation, enlarge its knowledge base, 
and test its performance with a sufficiently large 
set of sample texts. 
The major contribution of the work reported in 
the paper can be found in the novel proposed 
approach to importance evaluation that, according 
to the results so far achieved, proved to be viable 
and appropriate both from the cognitive and the 
computational points of view. 
249 
The research has disclosed several new 
directions for future work. Among these we 
mention: 
extending the importance rule base to 
cover the rhetoric and stylistic aspects 
of the text; 
introducing meta-rules to deal with the 
problems of rule activation scheduling, 
and of conflict resolution among rules; 
improving the goal matching techniques in 
order to implement a flexible mechanism 
for interpreting the content of 
encyclopedia frames according to the 
current goal; 
giving the evaluator the capability of 
changing the goal during the evaluation 
process, depending on the content of the 
processed text. 
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