Exploiting rhetorical relations to

International Journal of Network Security & Its Applications (IJNSA) Vol.7, No.2, March 2015
DOI : 10.5121/ijnsa.2015.7201 1
EXPLOITING RHETORICAL RELATIONS TO
MULTIPLE DOCUMENTS TEXT SUMMARIZATION
N. Adilah Hanin Zahri1
, Fumiyo Fukumoto2
, Matsyoshi Suguru2
and Ong Bi Lynn1
1
School of Computer and Communication,
University of Malaysia Perlis, Perlis, Malaysia
2
Interdisplinary Graduate School of Medicine and Engineering,
University of Yamanashi, Yamanashi, Japan
ABSTRACT
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many
applications such as text summarization, question answering and natural language generation. This work
proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for
cluster-based text summarization of multiple documents. We exploited rhetorical relations exist between
sentences to group similar sentences into multiple clusters to identify themes of common information. The
candidate summary were extracted from these clusters. Then, cluster-based text summarization is
performed using Conditional Markov Random Walk Model to measure the saliency scores of the candidate
summary. We evaluated our method by measuring the cohesion and separation of the clusters constructed
by exploiting rhetorical relations and ROUGE score of generated summaries. The experimental result
shows that our method performed well which shows promising potential of applying rhetorical relation in
text clustering which benefits text summarization of multiple documents.
KEYWORDS
Rhetorical Relations, Text Clustering, Extractive Text Summarization, Support Vector Machine, Probability
Model, Markov Random Walk Model
1.INTRODUCTION
The study on rhetorical relations between sentences has been introduced to analyze, understand,
and generate natural human-languages. Rhetorical relations hold sentences or phrases in a
coherent discourse and indicate the informative relations regarding an event i.e. something that
occurs at a specific place and time associated with some specific actions. Rhetorical relations are
defined according to the objective expression the writer intends to achieve by presenting two text
spans. There are several structures have been developed to describe the semantic relations
between words, phrases and sentences such as Rhetorical Structure Theory (RST) [1], RST
Treebank [2], Lexicalized Tree-Adjoining Grammar based discourse [3], Cross-document
Structure Theory (CST) [4][5] and Discourse GraphBank [6]. Each structure defines different
kind of relations to distinguish how events in text are related by identifying the transition point of
a relation from one text span to another. In general, rhetorical relations is defined by the effect of
the relations, and also by different constrains that must be satisfied in order to achieve this effect,
and these are specified using a mixture of propositional and intentional language. For instance, in
RST structure, the Motivation relation specifies that one of the spans presents an action to be

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performed by the reader; the Evidence relation indicates an event (claim), which describes the
information to increase the reader’s belief of why the event occurred [2]. Rhetorical relations also
describe the reference to the propositional content of spans and which span is more central to the
writer's purposes.
The interpretation of how the phrases, clauses, and texts are semantically related to each other
described by rhetorical relations is crucial to retrieve important information from text spans.
Previous works have proven that these kind of coherent structures have benefit text
summarization [7][8][9][10][11][12]. Text summarization is a process of automatically creating a
summary that retains only the relevant information of the original document. Generating
summary includes identifying the most important pieces of information from the document,
omitting irrelevant information and minimizing details. Automatic document summarization has
become an important research area in natural language processing (NLP), due to the accelerating
rate of data growth on the Internet. Text summarization limits the need for user to access the
original documents and improves the efficiency of the information search. The task becomes
tougher to accomplish as the system also has to deal with multi-document phenomena, such as
paraphrasing and overlaps, caused by repeated similar information in the document sets.
In general, rhetorical relations are used to produce optimum ordering of sentences in a document
and remove redundancy from generated summaries.
Our work focused on different aspect of utilizing rhetorical relations to enhanced text
summarization. In our study, we discovered that rhetorical relations not only describes how two
sentences are semantically connected, but also shows the similarity pattern between two
sentences. For instance, CST suggests that two text span connected as Paraphrase is offering
same information, and on the other hand, two text span connected as Overlap is having partial
similar information, as shown in Example 1 and Example 2 which adopted from CST structure:
Example 1: Paraphrase
S1 Smokes billows from the Pirelli building.
S2 Smoke rises from the Milan skyscraper.
Example 2: Overlap
S3 The plane put a hole in the 25th
floor of the Pirelli building, and smoke was seen pouring
from the opening.
S4 The plane crashed into 25th
floor of the Pirelli building in downtown Milan.
Figure 1 and 2 exhibit the illustration of both Paraphrase and Overlap using set theory diagram.
S2
S1
Figure 1. Similarity pattern of Paraphrase, where S1≈S2

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Figure 2. Similarity pattern of Overlap, where 3Ss and 4Ss
Figure 1 and 2 show that the similarity patterns between two sentences can be extracted from
rhetorical relations which can be exploited during construction of similar text clusters to identify
theme of common information in multiple documents for text summarization. Our objective is to
improve the retrieval of candidate summary from clusters of similar texts and utilize the rhetorical
relations to eliminate redundancy during summary generation.
We first examined and investigated the definition of rhetorical relations from existed structure
and then redefined the rhetorical relations between sentences which will be useful for text
summarization. We then perform an automated identification of rhetorical relations among
sentences from the documents using machine learning technique, SVMs. We examined the
surface features, i.e. the lexical and syntactic features of the text spans to identify characteristics
of each rhetorical relation and provide them to SVMs for learning and classification module. We
extended our work to the application of rhetorical relations in cluster-based text summarization.
The next section provides an overview of the existing techniques. Section 3 describes the
methodology of our system and finally, we report experimental result with some discussion.
2. PREVIOUS WORK
The coherent structure of rhetorical relations has been widely used to enhance the summary
generation of multiple documents [13][14][15]. For instance, a paradigm of multi-document
analysis, CST has been proposed as a basis approach to deal with multi-document phenomenon,
such as redundancy and overlapping information during summary generation [8][9][10][11][12].
Many of CST based works proposed multi-document summarization guided by user preferences,
such as summary length, type of information and chronological ordering of facts. One of the
CST-based text summarization approaches is the incorporation of CST relations with MEAD
summarizer [8]. This method proposes the enhancement of text summarization by replacing low-
salience sentences with sentences that have maximum numbers of CST relationship in the final
summary. They also observed the effect of different CST relationships against summary
extraction. The most recent work is a deep knowledge approach system, CST-based SUMMarizer
or known as CSTSumm [11]. Using CST-analyzed document, the system ranks input sentences
according to the number of CST relations exist between sentences. Then, the content selection is
performed according to the user preferences, and a multi-document summary is produced
CSTSumm shows a great capability of producing informative summaries since the system deals
better with multi-document phenomena, such as redundancy and contradiction. Most of the CST-
based works observed the effects of individual CST relationships to the summary generation, and
focuses on the user preference based summarization. Most of the corpus used in the previous
works was manually annotated for CST relationships. In other words, this technique requires deep
linguistic knowledge and manually annotated corpus by human.
On the other hand, cluster-based approaches have been proposed to generate summary with wide
diversity of each topic discussed in a multiple document. A cluster-based summarization groups
S4S3

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the similar textual units into multiple clusters to identify themes of common information and
candidates summary are extracted from these clusters [16][17][18]. Centroid based
summarization method groups the sentences closest to the centroid in to a single cluster [9][19].
Since the centroid based summarization approach ranks sentences based on their similarity to the
same centroid, the similar sentences often ranked closely to each other causing redundancy in
final summary. In accordance to this problem, MMR [20] is proposed to remove redundancies
and re-rank the sentences ordering. In contrast, the multi-cluster summarization approach divides
the input set of text documents in to a number of clusters (sub-topics or themes) and
representative of each cluster is selected to overcome redundancy issue [30]. Another work
proposed a sentences-clustering algorithm, SimFinder [21][22] clusters sentences into several
cluster referred as themes. The sentence clustering is performed according to linguistic features
trained using a statistical decision [23]. Some work observed time order and text order during
summary generation [24]. Other work focused on how clustering algorithm and representative
object selection from clusters affects the multi-document summarization performance [25]. The
main issue raised in multi-cluster summarization is that the topic themes are usually not equally
important. Thus, the sentences in an important theme cluster are considered more salient than the
sentences in a trivial theme cluster. In accordance to this issue, previous work suggested two
models, which are Cluster-based Conditional Markov Random Walk Model (Cluster-based
CMRW) and Cluster-based HITS Model [26]. The Markov Random Walk Model (MRWM) has
been successfully used for multi-document summarization by making use of the “voting” between
sentences in the documents [27][28][29]. Differ with former model, Cluster-based CMRW
incorporates the cluster-level information into the link graph, meanwhile Cluster-based HITS
Model considers the clusters and sentences as hubs and authorities [26].
3. FRAMEWORK
3.1. Redefinition of Rhetorical Relations
Our main objective is to exploit rhetorical relations in order to build clusters of similar text that
will enhance text summarization. Therefore, in this work, we make used the existing coherent
structure of rhetorical relations. Since that previous works proposed various structure and
definition of rhetorical relations, the structure that defines rhetorical relations between two text
spans is mostly appropriate to achieve our objective. Therefore, we adopted the definition of
rhetorical relation by CST [5] and examined them in order to select the relevant rhetorical
relations for text summarization. According to the definition by CST, some of the relationship
presents similar surface characteristics. Relations such as Paraphrase, Modality and Attribution
share similar characteristic of information content with Identity except for the different version of
event description. Consider the following examples:
Example 3
S5 Airbus has built more than 1,000 single-aisle 320-family planes.
S6 It has built more than 1,000 single-aisle 320-family planes.
Example 4
S7 Ali Ahmedi, a spokesman for Gulf Air, said there was no indication the pilot was
planning an emergency landing.
S8 But Ali Ahmedi said there was no indication the pilot was anticipating an emergency
landing.

5
Example 3 and 4 demonstrate an example of sentences pair that can be categorized as Identity,
Paraphrase, Modality and Attribution relations. The similarity of lexical and information in each
sentences pair is high, therefore these relations can be concluded as presenting the similar
relation. We also discovered similarity between Elaboration and Follow-up relations defined by
CST. Consider the following example:
Example 5
S9 The crash put a hole in the 25th floor of the Pirelli building, and smoke was seen
pouring from the opening.
S10 A small plane crashed into the 25th floor of a skyscraper in downtown Milan today.
Example 5 shows that both sentences can be categorized as Elaboration and Follow-up, where S9
describes additional information since event in S10 occurred. Another example of rhetorical
relations that share similar pattern is Subsumption and Elaboration, as shown in Example 6 and
Example 7, respectively.
Example 6
S11 Police were trying to keep people away, and many ambulances were at the scene.
S12 Police and ambulance were at the scene.
Example 7
S13 The building houses government offices and is next to the city's central train station.
S14 The building houses the regional government offices, authorities said.
S11 contains additional information of S12 in Example 6, hence describes that sentences pair
connected as Subsumption can also be defined as Elaboration. However, the sentences pair
belongs to Elaboration in Example 7 cannot be defined as Subsumption. The definition of
Subsumption denotes the second sentence as the subset of the first sentence, however, in
Elaboration, the second sentence is not necessary a subset of the first sentence. Therefore, we
keep Subsumption and Elaboration as two different relations so that we can precisely perform the
automated identification of both relations.
We redefined the definition of the rhetorical relations adopted from CST, and combined the
relations that resemble each other which have been suggested in our previous work [30].
Fulfillment relation refers to sentence pair which asserts the occurrence of predicted event, where
overlapped information present in both sentences. Therefore, we considered Fulfillment and
Overlap as one type of relation. As for Change of Perspective, Contradiction and Reader Profile,
these relations generally refer to sentence pairs presenting different information regarding the
same subject. Thus, we simply merged these relations as one group. We also combined
Description and Historical Background, as both type of relations provide description (historical
or present) of an event. We combined similar relations as one type and redefine these combined
relations. Rhetorical relations and their taxonomy used in this work is concluded in Table 1.

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Table 1. Type and definition of rhetorical relations adopted from CST.
Relations by CST Proposed Relations Definition of Proposed Relation
Identity, Paraphrase,
Modality, Attribution
Identity
Two text spans have the same information
content
Subsumption, Indirect
Speech, Citation
Subsumption
S1 contains all information in S2, plus
other additional information not in S2
Elaboration, Follow-up
Elaboration
S1 elaborates or provide more information
given generally in S2.
Overlap, Fullfillment
Overlap
S1 provides facts X and Y while S2
provides facts X and Z; X, Y, and Z
should all be non-trivial
Change of Perspective,
Contradiction, Reader
Profile
Change of Topics
S1 and S2 provide different facts about the
same entity.
Description, Historical
Background
Description
S1 gives historical context or describes an
entity mentioned in S2.
- No Relations No relation exits between S1 and S2.
By definition, although Change of Topics and Description does not accommodate the purpose of
text clustering, we still included these relations for evaluation. We also added No Relation to the
type of relations used in this work. We combined the 18 types of relations by CST into 7 types,
which we assumed that it is enough to evaluate the potential of rhetorical relation in cluster-based
text summarization.
3.2. Identification of Rhetorical Relations
The type of relations exist among sentences from multiple documents are identified by using a
machine learning approach, Support Vector Machine (SVMs) [31]. This technique is adopted
from our previous work [30], where we used CST-annotated sentences pair obtained from CST
Bank1
[5] as training data for the SVMs. Each data is classified into one of two classes, where we
defined the value of the features to be 0 or 1. Features with more than 2 value will be normalized
into [0,1] range. This value will be represented by 10 dimensional space of a 2 value vector,
where the value will be divided into 10 value range of [0.0,0.1], [0.1,0.2], …, [0.9,1.0]. For
example, if the feature of text span Sj is 0.45, the surface features vector will be set into
0001000000. We extracted 2 types of surface characteristic from both sentences, which are
lexical similarity between sentences and the sentence properties. Although the similarity of
information between sentences can be determined only with lexical similarity, we also included
sentences properties as features to emphasis which sentences provide richer and specific
information, e.g. location and time of the event. We provided these surface characteristics to
SVMs for learning and classification of the text span S1 according to the given text span S2
3.2.1 Lexical Similarity between Sentences
More than one similarity measurements is used to measure the amount of overlapping information
among sentences. Each measurement computes similarity between sentences from different
aspects.
1. Cosine Similarity
Cosine similarity measurement is defined as follows:
1
http://tangra.si.umich.edu/clair/CSTBank/phase1.htm

7
i ii i
i ii
ss
ss
SS
2
,2
2
,1
,2,1
21
)()(
)(
),cos(
where S1 and S2 represents the frequency vector of the sentence pair, S1 and S2, respectively.
The cosine similarity metric measures the correlation between the two sentences according to
frequency vector of words in both sentences. We observed the similarity of word contents,
verb tokens, adjective tokens and bigram words from each sentences pair. The cosine
similarity of bigram s is measured to determine the similarity of word sequence in sentences.
The words ordering indirectly determine the semantic meaning in sentences.
2. Overlap ratio of words from S1 in S2 , and vice versa
The overlap ratio is measured to identify whether all the words in S2 are also appear in S1, and
vice versa. This measurement will determine how much the sentences match with each other.
For instance, given the sentences pair with relations of Subsumption, the ratio of words from
S2 appear in S1 will be higher than the ratio of words from S1 appear in Ss. We add this
measurement because cosine similarity does not extract this characteristic from sentences. The
overlap ratio is measured as follows:
)(
),(#
)(
1
21
1
Swords
SSscommonword
SWOL
where “#commonword” and “#words” represent the number of matching words and the
number of words in a sentence, respectively. The feature with higher overlap ratio is set to 1,
and 0 for lower value. We measured the overlap ratio against both S1 and S2.
3. Longest Common Substring
Longest Common Substring metric retrieves the maximum length of matching word sequence
against S1, given two text span, S1 and S2, .
)(
)),((
)(
1
21
1
Slength
SStringMaxComSubslen
SLCS
The metric value shows if both sentences are using the same phrase or term, which will benefit
the identification of Overlap or Subsumption.
4. Ratio overlap of grammatical relationship for S1
We used a broad-coverage parser of English language, MINIPAR [32] to parse S1 and S2, and
extract the grammatical relationship between words in the text span. Here we extracted the
number of surface subject and the subject of verb (subject) and object of verbs(object). We
then compared the grammatical relationship in S1 which occur in S2, compute as follows:

8
)(1
),(1#
)(
1
21
1
SSubj
SScommonSubj
SSubjOve
)(1
),(1#
)(
1
21
1
SObj
SScommonObj
SObjOve
The ratio value describes whether S2 provides information regarding the same entity of S1 , i.e.
Change of Topics. We also compared the subject in S1 with noun of S2 to examine if S1 is
discussing topics about S2.
)(
)()(#
)(
1
21
1
SObj
SNounScommonSubj
SeSubjNounOv
The ratio value will show if S1 is describing information regarding subject mention in S2,, i.e.
Description.
3.2.2 Sentences Properties
The type of information described in two text spans is also crucial to classify the type of
discourse relation. Thus, we extracted the following information as additional features for
each relation.
1. Number of entities
Sentences describing an event often offer information such as the place where the event
occurs (location), the party involves (person, organization or subject), or when the event takes
place (time and date). The occurrences of such entities can indicate how informative the
sentence can be, thus can enhance the classification of relation between sentences. Therefore,
we derived these entities from sentences, and compared the number of entities between them.
We used Information Stanford NER (CRF Classifier: 2012 Version) of Named Entity
Recognizer [46] to label sequence of words indicating 7 types of entities (PERSON,
ORGANIZATION, LOCATION, TIME, DATE, MONEY and PERCENT).
The Stanford NER generally retrieves proper nouns from corresponding sentences and
categorize into one of the mentioned class, as shown in the following example:
On Jan./DATE 5/DATE, a 15-year-old boy crashed a stolen plane into a building in Tampa
/LOCATION, Florida/LOCATION.
As Stanford NER only recognizes proper nouns, the common noun such as “boy”' in the context
is not labeled as PERSON. Thus, in order to harvest maximum information from a text span, we
make use of the lexical units obtained from lexical database, FrameNet [33]. We extracted lexical
unit from FrameNet which matches the 7 class defined by Stanford NER class. The manual
lexical unit extraction is carried out by 2 human judges. Table 2 shows the example of frames
used in the experiment. We used data from FrameNet to retrieve the unidentified type of
information from common noun in sentences. We hereafter refer to the information retrieved here
and by Stanford NER as sentences entity. We computed the number of sentences entities
appearing in both S1 and S2. Based on the study of training data from CSTBank1
[5], there are no
significant examples of annotated sentences indicates which entity points to any particular

9
discourse relation. Therefore, in the experiment, we only observed the number of sentences
entities in both text spans. The features with higher number of entities are set to 1, and 0 for lower
value.
Table 2. Information adopted from FrameNet
NER Class
FrameNet
No.
Frames
Example of Frames
PERSON 12 People (e.g. person, lady, boy, man, woman)
People by vocation (e.g. police officer, journalist)
Behind the scene (e.g. film-maker, director, producer)
Kinship (e.g. father, mother, sister, brother)
Leadership (e.g. captain, chairman, president, chief)
Origin (e.g. European, Dutch, American, Chinese)
People by residence (e.g. roommate, neighbour,
housemate)
ORGANIZATION 6 Business (e.g. company, corporation, firm)
Organization (e.g. government, agency, committee)
Military (e.g. army, naval, military, navy)
LOCATION 12 Building (e.g. pyramid, airport, terminal, house)
Locale by event (e.g. theatre, battlefield, venue)
Locale by ownership (e.g. land, estate, property)
Locale by use (e.g. museum, gallery, college,
headquarters)
Part Orientational (e.g. west, east, north)
Political Locale (e.g. village, municipality, city)
TIME 2 Calenderic unit (e.g. morning, evening, noon)
Location in time (e.g. time)
DATE 2 Calenderic unit (e.g. winter, spring, summer)
Natural fatures (e.g. spring, fall)
MONEY 1 Money (e.g. money, cash, funds)
PERCENT 0 -
2. Number of conjunctions
We observed the occurrence of 40 types of conjunctions. We measured the number of
conjunctions appear in both S1 and S2, and compare which sentence contains more
conjunctions. We assumed that the higher the number of conjunctions, the more information
is provided in the corresponding text span. The comparison of the number of conjunctions
will help to determine relation i.e. Elaboration.
Table 3. List of conjunctions
because since now that as in order that
so so that why although though
even though whereas while but if
unless whether or not even if in case after
and before but for nor
once only if until when whenever
where wherever yet or either or
neither nor whether or not only but also both and

10
3. Lengths of sentences
We define the length of Sj by the number of word occurs in the corresponding text span, and
compare the length of both sentences. The length of both text spans will show whether both
text span are Identity, where the length will be the same, or one of the text spans presents
more information than another, where S1 will be longer, i.e. Subsumption.
We defined the length of Sj as follows:
i
ij wSLength )(
where w is the word appearing in the corresponding text span.
4. Type of Speech
We determined the type of speech, whether the text span, S1 cites another sentence by
detecting the occurrence of quotation marks to identify Citation or Indirect Speech which
are the sub-category of Identity.
3.3. Rhetorical Relation-based Text Clustering
The aim of this work is to expand the benefits of rhetorical relations between sentences to cluster-
based text summarization. Rhetorical relation between sentences not only indicates how two
sentences are connected to each other, but also shows the similarity patterns in both sentences.
Therefore, by exploiting these characteristics, our idea is to construct similar text clustering based
on rhetorical relations among sentences. We consider that the following relations are most
appropriate for this task:
(i) Identity
(ii) Subsumption
(iii) Elaboration
(iv) Overlap
These relations indicates either equivalence or partial overlapping information between text
spans, as shown in Table 1. Connections between two sentences can be represented by multiple
rhetorical relations. For instance, in some cases, sentences defined as Subsumption can also be
define as Identity. Applying the same process against the same sentence pairs will be redundant.
Therefore to reduce redundancy, we assigned the strongest relation to represent each connection
between 2 sentences according to the following order:
(i) whether both sentences are identical or not
(ii) whether one sentence includes another
(iii) whether both sentences share partial information
(iv) whether both sentences share the same subject of topic
(v) whether one sentence discusses any entity mentioned in another
The priority of the rhetorical relations assignment can be concluded as follows:
Identity > Subsumption > Elaboration > Overlap

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We then performed clustering algorithm to construct groups of similar sentences. The algorithm
is summarized as follows:
i) Rhetorical relations identified by SVMs is assign to between two sentences. For sentences
pair which is assigned with multiple relations, the strongest relations is assigned as stated
in the above (refer to Figure 3(a)).
ii) Suppose each sentence is a centroid of its own cluster. Sentences connected to the
centroid as Identity (ID), Subsumption (SUB), Elaboration (ELA) and Overlap
(OVE) relations is identified and sentences with these connections are evaluated as having
similar content, and aggregated as one cluster (refer Figure 3(b)).
iii) Similar clusters is removed by retrieving centroids connected as Identity, Subsumption or
Elaboration.
iv) Clusters from (iii) is merged to minimize the occurrence of the same sentences in multiple
clusters (refer Figure 3(c)).
v) Step (iii) and (iv) are iterated until the number of clusters is convergence
The algorithm of similar text clustering is illustrated in Figure 3. In this work, we performed and
observed 2 types of text clustering, which are:
i) RRCluster 1, which consist of Identity (ID), Subsumption (SUB), Elaboration (ELA)
and Overlap (OVE)
ii) RRCluster2, which consist of Identity (ID), Subsumption (SUB) and Elaboration (ELA)

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S1S3
S5
ELA
S6
ID
S7
ELA
GROUP A_B
…
…
…
…
S4
OVE
SUB
S2
S1S3 S4
S5
SUB
ELA
DES
OVE
S6
ID
S7
ELA
GROUP A
GROUP B
…
…
…
…
…
S2
S1S3 S4
S5
SUB
ELA
DES
OVE
…
…
…
…
…
S2
S1S3 S4
S5
SUB
ELA
DES
OVE
…
…
…
…
…
(a) (b)
(c)(d)
Figure 3. Rhetorical relation-based clustering algorithm
3.4. Cluster-based Summary Generation
We performed a cluster-based text summarization using clusters of similar text constructed by
exploiting rhetorical relations between sentences. We used Cluster-based Conditional Markov
Random Walk Model [26] to measure the saliency scores of candidate summary. Here we defined
the centroid as relevant candidate summary since each centroid represents the whole cluster. The
Conditional Markov Random Walk Model is based on the two-layer link graph including both the
sentences and the clusters. Therefore, the presentation of the two layer graph are is denoted as
SCSSCs EEVVG ,,,* . Suppose is vVV is the set of sentences and jc cCV is the set
of hidden nodes representing the detected theme clusters, where siijSS VveEE | corresponds
to all links between sentences. )(,,| ijcjsiijSC vcluscVcVveE corresponds to the
correlation between a sentence and its cluster. The score is computed measured as follows:
||
)1(~
)( *
,
V
MvSenScoreSenScore iij
ijall
j
µ is the damping factor set to 0.85, as defined in the PageRank algorithm. ijM ,
~
refers to row-
normalized matrix ||||
*
,
*
, )
~
(
~
VVijij MM to describe *~
G with each entry corresponding to the
transition probability, shown as follows:

13
))(),(|(
~ *
, iiiij vclusvclusjipM
Here, clus(vi) denotes the theme cluster containing sentence vi. The two factors are combined into
the transition probability from vi to vj defined as follows:
0,
))(),(|(
))(),(|(
))(),(|( ||
1
fif
vclusvcluskif
vclusvclusjif
vclusvclusjip V
k ki
ii
ii
))(),(|( ii vclusvclusjif denotes the new affinity weight between two sentences vi and vj,
where both sentences belong to the corresponding two clusters. The conditional affinity weight is
computed by linearly combining the affinity weight conditioned on the source cluster, i.e.
))(|( ivclusjif and the affinity weight conditioned on the target cluster i.e.
))(|( jvclusjif , defined in the following equation.
))(|()1())(|(())(),(|( iiii vclusjifvclusjifvclusvclusjif
))(,())(()( iii vclusvvclusjif
))(,())(()()1( jjj vclusvvclusjif
))(,())((()( iii vclusvvclusjif
))(,())(()1( jjj vclusvvclus
Where ]1,0[ is the combination of weight controlling the relative contributions from the
source cluster and the target cluster2
. ]1,0[))(( ivclus refers to the importance of cluster
)( ivclus in the whole document set D and ]1,0[))(,( ii vclusv denotes the strength of the
correlation between sentence iv and its cluster )( ivclus . In this work, ))(( ivclus is set to the
cosine similarity value between the cluster and the whole document set, computed as follows:
)),(())(( cos Dvclussimvclus iinei
Meanwhile, ))(,( ii vclusv is set to the cosine similarity value between the sentence and the
cluster where the sentence belongs, computed as follows:
))(,())(,( cos iiineii vclusvsimvclusv
The saliency scores for the sentences are iteratively computed until certain threshold, θ is
reached3
.

14
4.EXPERIMENT
4.1. Data
CST-annotated sentences are obtained from Cross-document Structure Theory Bank [5]. Our
system is evaluated using 2 data sets from Document Understanding Conference, which are
DUC'2001 and DUC'2002 [34].
4.2. Result and Discussion
4.2.1 Identification of Rhetorical Relations
SVMs classified the rhetorical relation of a sentence pair, S1 and S2, by considering the
relationship type of S1 according to S2, and vice versa. In this word, we focused on the strength of
the connection, rather than the number of the rhetorical relations belong to each connection. Since
that a sentence pair might contain multiple relations, we assigned the strongest relations to present
each connection. We conducted analysis to verify the most significant features against every
relation. We calculated the sum of the vector component products to evaluate the effectiveness of
each feature. The absolute value of weight directly reflects the importance of a feature in
discriminating the two classes. The easy interpretation of the obtained weight values allows to
identify the best features in case of a high-dimensional feature space. The evaluation results
shown in Table 4 demonstrates the top 5 of most significant features for each relation. For
instance, Identity indicates that both sentences are the same type of speech, which is indirect
speech, while the cosine similarity and word overlap metrics indicates a value of 0.7 and above.
From this evaluation, we concluded that the following features show most significant
characteristics during classification of most relations:
(i) Similarity measurements
(ii) Grammatical relationship
(iii)Number of entities
Table 4. Top 5 most significant features of each relations
Relations Significant Features
Identity Type of Speech (S1)= Indirect and Type of Speech (S1) = Indirect
0.7 ≤ Cosine similarity ≤0.8
0.9 ≤ Subject Overlap(S1) ≤ 1.0
Overlap Word (S2) ≥ Overlap Word (S1)
Named Entities (S1) ≥ Named Entities (S2)
Subsumption Length (S1) ≥ Length(S2)
Type of Speech (S1) = Indirect and Type of Speech (S1) = Indirect
0.2 ≤ Longest Common Substring ≤ 0.3
0.9 ≤ Subject Overlap (S1) ≤ 1.0
Elaboration Type of Speech (S1) = Indirect and Type of Speech (S1) = Indirect
Length (S1) ≥ Length (S2)
2
We set 5.0 for fair evaluation with methods adopted from (Wan and Yang, 2008)
3
In this study, the threshold, θ is set to 0.0001

15
Overlap Word (S2) ≥ Overlap Word(S1)
Overlap 0.9 ≤ Subject Overlap (S1) ≤ 1.0
0.1 ≤ Bigram similarity ≤ 0.2
0.2 ≤ Overlap Word (S2) ≤ 0.3
0.2 ≤ Cosine similarity ≤ 0.3
Change of Topic Type of Speech (S1) = Indirect and Type of Speech (S1) = Indirect
0.0 ≤ Cosine similarity ≤ 0.1
0.0 ≤ Overlap Word (S2) ≤ 0.1
Description Type of Speech (S1)= Indirect and Type of Speech (S1) = Indirect
Subject Overlap (S1) ≤ 0.0
Length (S2) ≥ Length (S1)
0.0 ≤ Bigram similarity ≤ 0.1
No Relations Subject Overlap (S1) ≤ 0.0
Subject Noun Overlap (S1) ≤ 0.0
0.0 ≤ Cosine Similarity ≤ 0.1
Bigram Similarity ≤ 0.0
Overlap Word (S1) ≤ 0.0
The rhetorical relations assigned by SVMs are manually evaluated by 2 human judges. Since no
human annotation is available for DUC data sets, 5 times of random sampling consisting 100
sentence pairs is performed against each document set of DUC'2001 and DUC'2002). The human
judges performed manual annotation against sentence pairs, and assessed if SVMs assigned the
correct rhetorical relation to each pair. The correct rhetorical relation refers to either one of the
relations assigned by human judges in case of multiple relations exist between the two sentences.
As a baseline method, the most frequent relation in each set of sampling data is assigned to all
sentence pairs. We evaluated the classification of rhetorical relations by measuring the Precision,
Recall and F-measure score.
Identity shows the most significant performance of Precision, where the value achieved more than
90% in both data sets. Meanwhile, the Precision value for Description performed the worst
compared to others in both data sets. As for Recall value, Identity, Subsumption, Elaboration and
Description yield more than 80%, meanwhile Change of Topic and No Relation performed the
worst with Recall of 60% in both data sets. We found that SVMs was unable to identify Change
of Topics, when multiple subjects (especially contained personal pronoun) occurred in a sentence.
According to F-Measure, SVMs performed well during the classification of Identity, Subsumption
and Elaboration with the Precision values achieved are above 70% for most data set. Overall,
compared to other relations, the Identity classification by SVMs performed the best in each
evaluation metric as expected. Sentence pair with Identity relation shows significant resemblance
in similarity value, grammatical relationship and number of entities. For instance, the similarity
between sentence pair is likely close to 1.0, and there are major overlap in subject and the object
of the sentences. Subsumption and Elaboration indicate promising potential of automated

16
classification using SVMs with F-measure achieved higher than 70%. We observed that
characteristics such as similarity between sentences, grammatical relationship and number of
entities are enough to determine the type of rhetorical relation of most data sets. Therefore, we
considered the ratio of rhetorical relations except for No Relations show a great potential for
automated classification with small number of annotated sentences.
We found that the lack of significant surface characteristic is the main reason of misclassification
of relations such as Overlap, Change of Topics and Description. Therefore, we conducted further
analysis using confusion matrix [35] to determine the accuracy of classification by SMVs.
Confusion matrix compares the classification results by the system and actual class defined by
human, which useful to identify the nature of the classification errors.
Table 6 and 7 describe the evaluation result of confusion matrix for DUC'2001 and DUC'2002,
respectively. The analysis is done against each relation independently. Each table shows the
classification nature of rhetorical relations according to the number of sentences pair. We also
included the accuracy and reliability value of every relations. For instance, according to
evaluation of DUC'2001 in Table 6, from 44 pairs of sentences with Identity relation, our system
has been able to classify 43 pairs of them as Identity correctly, while 1 pair misclassified as
Subsumption. As a result, the Accuracy and Reliability value achieved for Identity are 1.000 and
0.977, respectively.
Table 5. Evaluation result for identification of rhetorical relations
Relations
DUC’2001 DUC’2002
Precision Recall F-Measure Precision Recall F-Measure
Baseline 0.875 0.114 0.201 0.739 0.108 0.188
Identity 0.980 1.000 0.989 0.849 1.000 0.917
Subsumption 0.721 0.984 0.830 0.685 0.900 0.773
Elaboration 0.664 0.952 0.778 0.652 0.901 0.743
Overlap 0.875 0.532 0.653 0.739 0.556 0.633
Change of Topics 0.591 0.709 0.640 0.618 0.589 0.597
Description 0.841 0.947 0.886 0.817 0.856 0.826
No Relations 1.000 0.476 0.632 0.966 0.475 0.628
Table 6. Evaluation of Confusion Matrix for DUC’2001
Classification by System
Accuracy
ID SUB ELA OVE CHT DES NOR
Actual
Class
ID 43 0 0 0 0 0 0 1.000
SUB 1 61 0 0 0 0 0 0.984
ELA 0 2 48 0 0 1 0 0.941
OVE 0 3 12 57 3 2 0 0.533
CHT 0 5 6 6 51 3 0 0.718
DES 0 0 0 0 2 59 0 0.967
NOR 0 3 5 3 30 2 35 0.449
0.977 0.726 0.676 0.864 0.593 0.881 1.000
Table 7. Evaluation of Confusion Matrix for DUC’2002
Classification by System
Accuracy
ID SUB ELA OVE CHT DES NOR
Actual
Class
ID 55 0 0 0 0 0 0 1.000
SUB 6 51 0 0 0 0 0 0.895
ELA 0 4 35 0 0 0 0 0.897
OVE 2 12 6 54 2 2 0 0.557
CHT 1 4 9 10 40 2 1 0.597
DES 0 0 0 0 8 70 0 0.886
NOR 0 3 6 10 13 7 36 0.480
Reliability 0.859 0.689 0.614 0.730 0.635 0.864 0.973

17
Despite the errors discovered during the identification of rhetorical relations, the classification by
SVMs shows a promising potential especially for Identity, Subsumption, Elaboration and No
Relation. In future, the increment of annotated sentences with significant characteristics of each
relation will improve the identification of rhetorical relation. For instance, in this experiment,
Overlap refers to sentences pair that shares partial information with each other. Therefore, we
used Bigram similarity and Longest Common Substring metric to measure the word sequences in
sentences. However, these metrics caused sentences with long named entity, e.g. ``President
George Bush'' and ``Los Angeles'', as having consecutive words which contributed to false
positive result of Overlap relation. The increment of annotated sentences consists of consecutive
common nouns and verbs will help to precisely define Overlap relation. Moreover, improvement
such as the usage of lexical database to extract lexical chain and anaphora resolution tool can be
used to extract more characteristics from each relation.
4.2.2 Rhetorical Relation-based Clustering
We evaluated our method by measuring the cohesion and separation of the constructed clusters.
The cluster cohesion refers to how closely the sentences are related within a cluster, measured
using Sum of Squared Errors (SSE) [49]. The smaller value of SSE indicates that the sentences in
clusters are closer to each other. Meanwhile, Sum of Squares Between (SSB) [49] is used to
measure cluster separation in order to examine how distinct or well-separated a cluster from
others. The high value of SSB indicates that the sentences are well separated with each other.
Cosine similarity measurement is used to measure the similarity between sentences in both SSE
and SSB evaluation. We also obtained the average of Silhouette Coefficient (SC) value to
measure the harmonic mean of both cohesion and separation of the clusters [36][37]. The value
range of the Silhouette Coefficient is between 0 and 1, where the value closer to 1 is the better.
Table 8 shows the evaluation results for cohesion and separation of the clusters. RRCluster1
refers to the clusters constructed by Identity, Subsumption and Elaboration, while RRCluster1
refers to the clusters constructed by Identity, Subsumption, Elaboration and Overlap. We also
used K-Means clustering for comparison [38]. K-means iteratively reassigns sentences to the
closest clusters until a convergence criterion is met. Table 8 indicates that RRCluster2, which
generates clusters of sentences with strong connections Identity, Subsumption and Elaboration,
demonstrates the best SSE value (4.181 for DUC'2001 and 3.624 for DUC'2002), which shows
the most significant cohesion within clusters. In contrast, RRCluster1 which includes Overlap
during clustering indicates the most significant separation between clusters with the best SSB
value (397.237 for DUC'2001 and 257.118 for DUC'2002). RRCluster1 generated bigger
clusters, therefore resulted wider separation from other clusters. The average Silhouette
Coefficient shows that our method, RRCluster1 (0.652 for DUC'2001 and 0.636 for DUC'2002)
and RRCluster2 (0.628 for DUC'2001 and 0.639 for DUC'2002) outranked K-Means (0.512 for
DUC'2001 and 0.510 for DUC'2002) for both data sets.
In addition, we examined the clusters by performing a pair-wise evaluation. We sampled 5 sets of
data consisting 100 sentences pairs and assessed if both sentences are actually belong to the same
clusters. Table 9 shows the macro average Precision, Recall and F-measure for pair-wise
evaluation. RRCluster2, which excludes Overlap relation during clustering, demonstrated a lower
Recall value compared to RRCluster1 and K-Means. However, the Precision score of RRCluster2
indicates better performance compared to K-Means. Overall, RRCluster1 obtained the best value
for all measurement compared to RRCluster2 and K-Means for both data sets. We achieved
optimum pair-wise results by including Overlap during clustering, where the F-measure obtained
for DUC'2001 and DUC'2002 are 0.770 and 0.766, respectively.

18
Table 8. Evaluation result for cohesion and separation of clusters
Data Set Evaluation
Clustering Method
K-Means
RRCluster1
(ID,SUB,ELA,OVE)
RRCluster2
(ID, SUB, ELA)
DUC’2001 Average SSE 7.271 4.599 4.181
Average SSB 209.111 397.237 308.153
Average SC 0.512 0.652 0.628
DUC’2002 Average SSE 6.991 3.927 3.624
Average SSB 154.511 257.118 214.762
Average SC 0.510 0.636 0.639
Table 9. Evaluation result for pair-wise
Data Set Evaluation
Clustering Method
K-Means
RRCluster1
(ID,SUB,ELA,OVE)
RRCluster2
(ID, SUB, ELA)
DUC’2001 Precision 0.577 0.783 0.805
Recall 0.898 0.758 0.590
F-Measure 0.702 0.770 0.678
DUC’2002 Precision 0.603 0.779 0.750
Recall 0.885 0.752 0.533
F-Measure 0.716 0.766 0.623
We made more detailed comparison between clusters constructed by K-Means and our method.
The example of the clustered sentences by each method from the experiment is shown in Table
10. K-Means is a lexical based clustering method, where sentences with similar lexical often be
clustered as one group although the content semantically different. The 5th
sentences from K-
Means cluster in Table 10 demonstrates this error. Meanwhile, our system, RRCluster1 and
RRCluster2 performed more strict method where not only lexical similarity, but also syntactic
similarity, i.e the overlap of grammatical relationship is taken into account during clustering.
According to Table 8, Table 9 and Table 10, the connection between sentences can allow text
clustering according to the user preference. For instance, RRCluster2 performed small group of
similar sentences with strong cohesion in a cluster. In contrast, RRCluster1 method performed
clustering of sentences with Identity, Subsumption, Elaboration and Overlap, which are less strict
than RRCluster2, however presents strong separation between clusters. In other words, the
overlapping information between clusters are lower compared to RRCluster2. Thus, the
experimental results demonstrate that the utilization of rhetorical relations can be another
alternative of cluster construction other than only observing word distribution in corpus.
Table 10. Comparison of sentences from K-Means and proposed methods clusters
K-Means
√ Centroid
Tropical Storm Gilbert formed in the eastern Caribbean and strengthened into a hurricane
Saturday night.
√ 1
Earlier Wednesday Gilbert was classified as a Category 5 storm, the strongest and deadliest
type of hurricane.
√ 2
Such storms have maximum sustained winds greater than 155 mph and can cause
catastrophic damage.
√ 3
As Gilbert moved away from the Yucatan Peninsula Wednesday night , the hurricane formed
a double eye, two concentric circles of thunderstorms often characteristic of a strong storm
that has crossed land and is moving over the water again.
√ 4
Only two Category 5 hurricanes have hit the United States the 1935 storm that killed 408
people in Florida and Hurricane Camille that devastated the Mississippi coast in 1969,
killing 256 people.
x 5
“Any time you contract an air mass , they will start spinning . That's what makes the
tornadoes , hurricanes and blizzards , those winter storms”,Bleck said.
RRCluster1
√ Centroid Tropical Storm Gilbert formed in the eastern Caribbean and strengthened into a hurricane

19
Saturday night.
√ 1
On Saturday, Hurricane Florence was downgraded to a tropical storm and its remnants
pushed inland from the U.S. Gulf Coast.
√ 2
The storm ripped the roofs off houses and flooded coastal areas of southwestern Puerto
Rico after reaching hurricane strength off the island's southeast Saturday night.
√ 3
Hurricane Gilbert, one of the strongest storms ever, slammed into the Yucatan Peninsula
Wednesday and leveled thatched homes, tore off roofs, uprooted trees and cut off the
Caribbean resorts of Cancun and Cozumel.
√ 4 It reached tropical storm status by Saturday and a hurricane Sunday.
√ 5
Saturday night.
RRCluster2
√ Centroid
Saturday night.
√ 1
On Saturday, Hurricane Florence was downgraded to a tropical storm and its remnants
pushed inland from the U.S. Gulf Coast.
√ 2
The storm ripped the roofs off houses and flooded coastal areas of southwestern Puerto
Rico after reaching hurricane strength off the island's southeast Saturday night.
√ 3 It reached tropical storm status by Saturday and a hurricane Sunday.
√ 4
Saturday night.
4.2.3 Cluster-based Summary Generation
We generated short summaries of 100 words for DUC'2001 and DUC'2002 to evaluate the
performance of our clustering method, and to observe if rhetorical relation-based clustering
benefits the multi-document text summarization. The experimental results also include the
evaluation of summaries based on clusters generated by Agglomerative Clustering, Divisive
Clustering and K-Means as comparison, adopted from [26]. The ROUGE-1 and ROUGE-2 score
of clustering method shown in Table 11.
For DUC'2001 data set, our RRCluster1 performed significantly well for ROUGE-1 and ROUGE-
2 score, where we outperformed others with highest score of 0.3602 and 0.0736, respectively.
Divisive performed the worst compared to other methods. As for DUC'2002 data set,
Agglomerative obtained the best score of ROUGE-1 with 0.3854, while RRCluster2 yield the
lowest score of 0.3591. In contrast, RRCluster1 gained the best score of ROUGE-2 with 0.0873.
We observed that our proposed RRCluster1 performed significantly well with ROUGE-2. During
the classification of rhetorical relations, we also considered word sequence of Bigram to
determine rhetorical relations, therefore resulted a high score of ROUGE-2. However, the
ROUGE-1 score of our proposed methods performed poorly for DUC'2002 data sets, especially
for RRCluster2. This technique, which considers Identity, Subsumption and Elaboration during
text clustering certainly constructed clusters with high cohesion, but also limits the clustering to
sentences with only strong connections. This lead to the construction of many small clusters with
possibility of partial overlaps of information with other clusters. As a result, the structure of
clusters in RRCluster2 caused the low value of both ROUGE-1 and ROUGE-2 scores.
Although our method only achieved good ROUGE-2 score, we considered that rhetorical relation-
based clustering shows a great potential since that our clustering method is at initial stage yet
already outperformed some of the well-established clustering method. Clearly, rhetorical relation-
based cluster need some further improvement in future in order to produce better result. However,
the result we obtained from this experiment shows that rhetorical relation-based clustering can
enhance the cluster-based summary generation.

20
Table 11. Comparison of ROUGE score for DUC’2001 and DUC’2002
Method
DUC’2001 DUC’2002
ROUGE-1 ROUGE-2 ROUGE-1 ROUGE-2
Agglomerative 0.3571 0.0655 0.3854 0.0865
Divisive 0.3555 0.0607 0.3799 0.0839
K-Means 0.3582 0.0646 0.3822 0.0832
RRCluster2 0.3359 0.0650 0.3591 0.0753
RRCluster1 0.3602 0.0736 0.3693 0.0873
5. CONCLUSIONS
This paper investigated the relevance and benefits of the rhetorical relation for summary
generation. We proposed the application of rhetorical relations exist between sentences to text
clustering which improved extractive summarization for multiple documents. This work focused
on the extraction of candidate summaries from generated clusters and redundancy elimination.
We examined the rhetorical relations from Cross-document Theory Structure (CST), then selected
and redefined the relations that benefits text summarization. We extracted surfaces features from
annotated sentences obtained from CST Bank and performed identification of 8 types of
rhetorical relations using SVMs. Then we performed similar text clustering by exploiting
rhetorical relations among sentences. We used ranking algorithm that include the cluster-level
information, Cluster-based Conditional Markov Random Walk (Cluster-based CMRW) to
measure the saliency score of candidates summary extracted from generated clusters. For
DUC'2001, our proposed method, RRCluster1 performed significantly well for ROUGE-1 and
ROUGE-2 score with highest score of 0.3602 and 0.0736, respectively. Meanwhile, RRCluster1
gained the best score of ROUGE-2 with 0.0873 for DUC'2002. This work has proved our theory
that rhetorical relations can benefit the similar text clustering which enhanced text summarization.
From the evaluation results, we concluded that the rhetorical relations are effective to construct
theme clusters of common information and eliminate redundant sentences. Furthermore, our
system does not rely on fully annotated corpus and does not require deep linguistic knowledge.
ACKNOWLEDGEMENTS
This research is supported by many individuals from multiple organization of University of
Yamanashi, Japan and University of Perlis, Malaysia.
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Authors
N. Adilah Hanin Zahri graduated from Computer Science and Media Engineering,
University of Yamanashi in 2006. She received MSc in 2009 and PhD in Human
Environmental Medical Engineering in 2013 from Interdisciplinary Graduate School of
Medicine and Engineering, University of Yamanashi, Japan. Currently, she is working at
Department of Computer Engineering, School of Computer and Communication
Engineering in University of Malaysia Perlis, Malaysia.
Fumiyo Fukumoto graduated from Department of Mathematics in the faculty of
Sciences, Gakushuin University, 1986. From 1986 to 1988, she joined R&D Department
of Oki Electric Industry Co., Ltd. From 1988 to 1992, she joined Institute for New
Generation Computer Technology (ICOT). She was at Centre for Computational
Linguistics of UMIST (University of Manchester Institute of Science and Technology), England as a
student and a visiting researcher, from 1992 to 1994, and awarded MSc. Since 1994, she has been working
at University of Yamanashi, Japan. She is a member of ANLP, ACL, ACM, IPSJ and IEICE.
Suguru Matsuyoshi received the B.S. degree from Kyoto University in 2003, and the
M.S. and Ph.D. degrees in informatics from Kyoto University, Japan, in 2005 and 2008,
respectively. Prior to 2011, he was a Research Assistant Professor in Graduate School of
Information Science, Nara Institute of Science and Technology, Ikoma, Japan. Since 2011, he has been an
Assistant Professor in Interdisciplinary Graduate School of Medicine and Engineering, University of
Yamanashi, Japan.
Ong Bi Lynn graduated with B. Eng. (Hons) Electrical and Electronics from Universiti
Malaysia Sabah (UMS) in the year 2001. She received her Master of Business
Administration from Universiti Utara Malaysia (UUM) in 2003. She obtained her Ph.D. in
the field of Computer Network in the year 2008 from Universiti Utara Malaysia (UUM).
Currently, she is working with Department of Computer Network Engineering, School of Computer and
Communication Engineering in Universiti of Malaysia Perlis (UniMAP), Perlis, Malaysia.

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