Multiple-text Summarization for Collective Knowledge Formation
Tomohiro FUKUHARA∗ , Hideaki TAKEDA∗ and Toyoaki NISHIDA∗∗∗

Graduate School of Information Science,
Nara Institute of Science and Technology
8916-5 Takayama, Ikoma city, Nara 630-0101, Japan
E-mail: {tomohi-f,takeda}@is.aist-nara.ac.jp∗∗

School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan
E-mail: nishida@kc.t.u-tokyo.ac.jp

Abstract
Multiple-text summarization method for facilitating aprocess of the collective knowledge formation is proposed. There are enormous pieces of information represented by WWW pages which contain various topics inearly stage of the community. To facilitate this process,organizing various unordered pieces of information isneeded. We facilitate this process by multiple-textsummarization. Proposed method consists of topic
identiﬁcation method and context-based summarization method. Topic identiﬁcation method ﬁnds a topic
including central information over the text set. We
identify partialities of topics based on skewness andkurtosis of a word frequency. Context-based summarization method generates summaries by linking relevant topics. Summarization in our approach is to ﬁnd
a context that is an ordered set of sentences. We ﬁnd acontext based on theme and focus of a sentence which
represent central information within a sentence. We
developed a prototype system called Topic Showcase.
Experimental results demonstrate the availabilities of
proposed methods in identifying topics from each cluster and supporting users in forecasting contents of thetexts.
1

Introduction

According to the wide spread of the network connectable electricities, a lot of network communities are
arising. The network community (we refer network
community as “the Community” below) here is an information resource that is consisted of human resources
and information resources. The human resource is regarded as an implicit knowledge such as know-hows orexperiences owned by members of the community who
have expertise in a particular domain. The information resource is an explicit knowledge such as email,discussions on BBS, databases shared by members of
the community.
One of the features of the community is the collective knowledge formation. The collective knowledge
is a knowledge constructed and maintained among themember of the community, such as FAQs, community’s

knowledge bases and other documents shared within
the community. The collective knowledge is formed
by gathering, ﬁltering and editing of the community’s
information resource.
One of the issues in a process of the collective
knowledge formation is a complexity of the community’s information resource. In early stage of the community, community’s information resource is unorganized because each member add information to thecommunity’s information resource planlessly. Accordingly, members can’t ﬁnd out an overview of the community’s information resource. Consequently, the process of the collective knowledge formation is delayed inearly stage of the community. Accelerating the processof the collective knowledge formation, organizing information by summarization of unorganized informationis needed.
We propose a multiple-text summarization method
for facilitating a process of the collective knowledge
formation. Summarizing community’s information resource, each member of the community can ﬁnd out
what kind of information has been discussed or shared
among the community. Proposed method consistsof 2 sub-methods, (1)topic identiﬁcation method and(2)context-based summarization method. Topic identiﬁcation method identiﬁes topics, which indicate crucial sentences among texts, based on statistical information of words in texts. Context-based summarization method generates a summary putting relevant sentences together as a context. A summary here is anordered set of sentences to which each sentence relevant.
In the remain of this paper, we ﬁrst analyze the process of the collective knowledge formation and discussof the necessity of the summarization. We then describe the proposed method: topic identiﬁcation basedon statistical information and summarization based on
the context. We show Topic Showcase: a prototype system for multiple-text summarization. We ﬁnally show
the results of an evaluation and discuss the results.

2

Multiple-text Summarization for Collective
Knowledge Formation

Summarizing multiple-text is needed for accelerating
the process of the collective knowledge. We ﬁrstly analyze the process of the collective knowledge and thenproposed multiple-text summarizing method.
2.1

Process of the Collective Knowledge Formation

The process of the collective knowledge formation consists of 3 steps.
Gathering Step
In this step, each member of the community gathers
information or describes their own knowledge into
documents. This process is mostly accomplished as
personnel and independent process.
Filtering Step
Each pieces of information are gathered and ﬁlteredin this process. Some pieces of information are ﬁltered and remains are shared among the community. This process feeds back to the previous process.
Describing Step
The collective knowledge is constructed according
to the discussion. Core members or editorial boardof the community are formed for describing knowledge as documents. The collective knowledge is
described as FAQs, WWW pages and knowledge
bases.
An issue that we focus in the process of the collective knowledge formation is how to organize a disordered community’s information resource. Various
kinds of information are included in the community’s
information resource in early stage of the community.
These pieces of information must be sorted and organized in order to be utilized as the collective knowledge.
However, it requires much time to organize these unsorted pieces of information. Accelerating the processof the community knowledge formation, ordering information is needed.
Related works of ordering the community’s information is proposed. Matsubara proposed CommunityBoard which is a system for visualizing the structureof discussions between members of the community[1].
CommunityBoard facilitates users to know what kindof topics have been discussed or who is discussing currently.
Hirata proposed CoMeMo-Community which is asystem for simulating discussions among members of acommunity[2]. CoMeMo-Community simulates discussions using agents who each have own memories which
represent their each owner’s knowledge. Discussions
between agents are performed by linking relevant memories to other memories.
These systems have merits on knowing a process ofthe collective knowledge formation, but don’t organize

Figure 1: Overview of multiple-text summarization.
information directly. Organizing information such asclassifying, extracting or summarizing information isneeded for accelerating the process of collective knowledge formation.
We propose a text summarization method. Our
aim in the text summarization is to assist membersof getting an overview of the community’s information resource. Community’s information resource inearly stage of the community is regarded as a unorganized information pool where members of the community hardly grasp all of information resources sharedin the community. Condensing an unorganized information pool into a summary, members can ﬁnd out
what kind of topics are discussed and focused in community’s information resource.
2.2

Multiple-text Summarization

We propose a multiple-text summarization method foraccelerating the process of the collective knowledge formation. One of the causes of delaying the process is
to comprehend the community’s information resource
including various kinds of topics. We regard community’s information resource as multiple-text including
various kinds of topics. Summarizing multiple-text,
the process of the collective knowledge formation isaccelerated.
There are related works on multiple-text summarization. McKeon proposed a method for summarizing
a series of news articles[3]. Proposed method is basedon information extraction which extracts speciﬁed information using cue words. Summary is formed basedon a comparison between extracted pieces of information.
Nanba proposed a method for summarizing a set ofscientiﬁc papers[4]. Proposed method utilizes a reference of citation among papers. Identifying a purposeof citation, proposed method generates a summary according to the purpose of reference.
One of the merits of these methods is that their
methods can product a detailed summary which considers the contents of text itself and relationships
among texts. However, these methods restrict texts

Formulas (1) and (2) show the deﬁnitions of skewness
and kurtosis.
n

α3

=

1  (xi − x̄)3
n i=1
s3

α4

=

1  (xi − x̄)4
−3
n
s4

(1)

n

(2)

i=1

Figure 2: Comparison of skewness and kurtosis amongwords within a general or partial topic.
types because their methods rely on speciﬁc domain
knowledge.
In the process of the collective knowledge formation, there are various types of texts from formal information such as news articles, research papers to informal information such as WWW pages, e-mails. Former methods have a limitation on summarizing texts
because they rely on domain knowledge. In summarizing community’s information resource which includes
various types of information, domain independent summarization method is needed.
We propose a multiple-text summarization method
being domain independent. We use statistical information of words in texts and identiﬁes topics whichindicate the points of texts. Figure 1 shows our approach.
Proposed method consists of 2 sub-methods.
Topic Identification
Topic identiﬁcation method identiﬁes topics. A
topic is a sentence indicating the points of texts.
We identify topics using statistical information ofwords from classiﬁed texts.
Context-based Summarization
Context-based summarization method generates asummary which has a context. A context here is arelationship between sentences and a summary isa set of topics which has sequential order originatedfrom source topic. We generate a summary for each
topic by linking relevant topics.
3

Topic Identification based on Statistical
Information

We identify topics based on classiﬁcation and statistical information of texts. We ﬁrstly describe that skewness and kurtsis indicate the partiality of topics among
entire text set, and then we show our proposed method.
3.1

Skewness and Kurtosis

Skewness and Kurtosis are both statistical measure.
Skewness indicates a distortion of a distribution andkurtosis indicates a centralization of a distribution.

x1 , x2 , · · · , xn are input data, x̄ is an average and s isa standard deviation.
Skewness and kurtosis show a diﬀerence between
general topic and partial topic. General topic is a
topic which indicates a common topic to the entire
texts. General topic is speciﬁed from all of texts. Partial topic is a topic which indicates a unique topic tosome parts of the entire texts. Partial topic is speciﬁed
from each category of classiﬁed texts.
Figure 2 shows an example that skewness and kurtosis indicate a diﬀerence between general topic andpartial topic. In ﬁgure 2, the words appeared in theupper part of the ﬁgure (i.e. “power plant” to “steam”)
are extracted from a general topic, and the words of thelower part of the ﬁgure (i.e. “wind power” to “technology”) are extracted from partial topics. We selected
these words manually from general topic and partial
topic retrieved by the keyword “wind generator”.
This ﬁgure shows that the words selected from partial topic indicate highly values against general topic.
Thus we can measure the partiality of the topic using
skewness and kurtosis. We use skewness and kurtosisof the words as measures for knowing the partiality ofeach topic.
3.2

Topic Identification

We identify topics by the following steps.
1. Classifying texts into categories
2. Calculating the partiality of each word
3. Identifying general topic and partial topic
At ﬁrst step, we classify texts into some clusters
in which each text similars to each other. We apply
hierarchical clustering method to the text set. We
use VSM(Vector Space Model) and cosine similarity
measure[5].
Accordingly, we calculate the partiality of eachword. The value of the partiality of each word is calculated in the next formula (3),P(termi ) =w1 α3 + w2 α4(3)where termi is ıi-th term in a text, w1 , w2 (w1 >0, w2 > 0) are weights for skewness(α3 ) andkurtosis(α4 ). P means the partiality of the word.
Finally, each topic is identiﬁed based on the partiality of the word. We identify a topic as a sentence.
We calculate a score for each topic using the formulaTable 1: Algorithm for the context formation.
Given:
i = 1, ci (source topic)
Return: C = {c1 , c2 , · · · , cN } (summary context)

Figure 3: Context formation by linking theme and focus.
(4).
Score(S) =mP(termi )

(4)

i=1

where S is a sentence in the text. We identify a topic byapplying thresholds (τg , τp (τp > τg )) to the Score(S).
Formula (5) is an evaluation function.

general(Score(S) ≤ τg )S =(5)partial(Score(S) ≥ τp )We identify general and partial topic using Score(S)and thresholds.
4

step 1 Repeat while i < N . Return a context C and exit the loop if i = N .
step 2 Seek a set of focuses F (ci ) =i} of a sentence ci . Select{f1i , f2i , · · · , fm
a focus fp from a set of focuses F (ci ).
step 3
Seek a set of topics S(fp ) ={si1 , si2 , · · · , sin } which take fp asa theme．Select a derivative topic
Select siq where
siq from S(fp ).
imin(Score(sq ))) ∧ siq
∈
/
C for
i
general topic.
Select sq where/ C for partial
max(Score(siq ))) ∧ siq ∈topic.
i = i + 1, ci = siq ．Return to step 1.

of context formation is shown in Figure 3. A summary consists of a source topic and derivative topics.
Source topic is a topic which becomes ﬁrst sentence
in a summary and derivative topic is a topic whichbecomes second to the last sentence in a summary.
Derivative topics are always relevant to the previous
topic in which the focus of the previous topic relevants
to the theme of the current topic.
Summarizing process consists of following process.

Context-based Summarization

For fascilitating the understandability of the summary,we think a coherence among sentences in the summaryis important. In this paper, we form a summary which
has a context where each sentence relevants to anothersentence. Context here is an order of sentences. We
form a summary context by linking the relevant topics(sentences).
We use theme and focus of a sentence for linking
relevant sentences. Theme is a subject of a sentence
and focus is an information which is emphasized in a
sentence. For example, in the following sentence Quickbrown fox jumped over the lazy dog., “Quick brown fox”
is the theme and “the lazy dog” is the focus.
We identify the theme and focus based on the caseof each clause. In the case grammar, each clause in a
sentence has a case such as subject and object whichindicates a function against a verb. In the previous
example, subject noun of the sentence is “fox” andobject noun is “dog”. We ﬁrst specify cases of eachclause in a sentence and then identify the theme andfocus 1 .
Context is formed by linking focus in a previous
sentence and theme in a current sentence. An idea
1 We specify cases using case analysis tool: KNP which analize
cases in a Japanese sentence. Below is the URL.
http://www-nagao.kuee.kyoto-u.ac.jp/nl-resource/knp-e.html

1. Specifying a source topic which becomes a ﬁrst sentence in a summary.
2. Finding a relevant topic to the source topic fromtexts and linking the relevant topic as a derivative
topic.
3. While the number of sentences included in a summary context is below N, repeat linking a topic
which is relevant to the previous topic.
Algorithm for the context formation is shown in
Table 1. In this algorithm, source topic (ci (i =1)) is given, and seeks a summary context (C ={c1 , c2 , · · · , cN }). Functions appeared in the algorithm
are F and S. F returns a focus which becomes a focus of the previous sentence. S returns a set of topics
whose themes are equal to the focus of the previous
topic. Algorithm returns the summary context when
the number of the topics is equal to N .
5

Topic Showcase

We implemented the topic identiﬁcation method andcontext-based summarization method to the prototype
system called Topic Showcase. Topic Showcase is amultiple-text summarization system which summarizes
the results of text retrieval. Screen image of the system
is shown in Figure 4 and Figure 5.

Figure 4: Screen image of Topic Showcase: topic mode.
System retrieves texts from news article2 . In thispaper, we regard news articles as a community’s information resource.
Main features of Topic Showcase are followed.
Retrieval and Clustering
System can retrieve texts by keywords and classiﬁes
the retrieval results into clusters.
Clustering Configuration
Users can change the number of the cluster at anytime.
Dynamic Topic Identification
System can identify topics dynamically according
to the change of the number of clusters.
Topic Configuration
Users can select a theme manually and browse
theme-related topic.
Dynamic Summarization
System can generate summaries dynamically according to the change of the theme of a summary.
6

Experiment and Discussion

We evaluate topics and summaries produced by Topic
Showcase. As a result, we got aﬃrmative answers in
the following evaluations.
• partial topics identiﬁed from each cluster• forecasting the contents from a summary• total evaluation of Topics Showcase
6.1

Description of the Experiment

We evaluated topics and summaries using Topic Showcase. Topics and summaries are produced by the system. Eight test subjects attended the experiment. All
2 Data of Topic Showcase is Japanese news articles.

Figure 5: Screen image of Topic Showcase: summary
mode.
test subjects are graduate students of information science.
We use 5 grades as an evaluation measure from Best
to Worst. We use given topics and user-selected summaries for evaluation. Given topics are generated from28 texts which is retrieved by the keyword “wind generator” in the evaluation of topics. In the evaluation ofsummaries, we allow users to select summaries freely.
6.2

TopicsGeneral evaluation of topics is shown in Figure 6. We
got 51.4% of aﬃrmative answers on partial topics. This
result indicates the proposed method is available for
identifying partial topic.
However, we got 50.0% of negative answers on general topics. We think the cause of the results is thesize of texts in which topics are speciﬁed. We identiﬁed general topic from 28 texts which is retrieved bysimple keyword matching. Finding general topic whichis common to all texts is diﬃcult because these texts
include various topics. To improve the method, evaluating method for consistency of texts is needed.
6.3

Summaries

To Facilitate a process of the collective knowledge formation, providing an overview of community’s information resource for members of the community is important. In this evaluation, we evaluated two criterion,
i.e., (1)possibilities in forecasting the contents of texts
from summaries and (2)agreement between forecasting
and actual texts. As a result, we got aﬃrmative answers in the question (1). Figure 7 shows the results.
We got 62.5% of aﬃrmative answers in forecasting.

This result shows that the proposed method is available in facilitating members to ﬁnd what kind of information is in the community’s information resource.
In the evaluation of the comparson between forecasts and the contents, we got 37.5% of aﬃrmative
answers and 25.0% of negative answers. This resultshows that test subjects understood the actual texts
wrongly from summaries. The main cause of this result is wrong context of summaries. Proposed method
simply produces context by linking the theme and thefocus. However, there are more relationships among
sentences. To improve the problem, analisys on the relationship among sentences and computational context
formation method is needed.
6.4

Figure 6: General evaluation for each topic.

General Evaluation of Topic Showcase

In this evaluation, we evaluated the possibility of the
Topic Showcase to support user’s comprehension of
texts. As a result, we got 75.0% of aﬃrmative answers in general evaluation of Topic Showcase. Figure
7 shows the result.
This results show a possibility for facilitating thecollective knowledge formation by providing topics andsummaries of community’s information resource.
To support user’s comprehension on the text set,
multiple-text summarization system should be developped in regarding the following points.
• information extraction from the texts which include
various topics.
• identiﬁcation of commonalities and diﬀerencesamong texts.
• media mixed summarization
First point is necessary for summarizing the text
set including various topic such as retrieval results from
WWW. Current summarization system limits the text
for preciseness of the summary. However, there is arequirement for summarizing full-text retrieval results
which include various topics. Summarizing various
texts is one of the problem.
Second point is necessary for comparing amongtexts precisely. We proposed a multiple-text summarization system for supporting user’s comprehension of
the text set. However, users of our system have to readoriginal texts for comparing the commonalities and differences among the texts. Identifying commonalities
and diﬀerences among texts is needed.
Third point is necessary for suporting user’s understanding of the texts. Current summarization system
only uses text for summary. Adding images and sounds
to simple text summary, user’s understanding will bemuch fascilitated.
Multiple-text summarization system is needed for
fascilitating the process of the collective knowledge formation. In this paper, we use news article as community’s information resource and evaluated the system’s

Figure 7: Evaluation for the summary.
possibilities. We’ll use this system in the real community and evaluate the possibilities of the multiple-textsummarization system.
7

Conclusion

We proposed a multiple-text summarization for facilitating the process of the collective knowledge formation. In early stage of the community, there are enormous pieces of information which include unorganizedand various topics. To facilitate the process, orderingcommunity’s information resource is needed. Our aim
is to facilitate the process by providing an overview
of community’s information resource for members of
the community. Experiments showed an availability
for ordering unorganized set of texts and a possibility
for facilitating community’s information resource.
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