Improving Readability for Tweet Contextualization using Bipartite

Graphs

Amira Dhokar, Lobna Hlaoua and Lotﬁ Ben Romdhane

SDM Research Group, MARS Research Lab ISITCom, University of Sousse, Tunisia

Keywords:

Bipartite Graphs, Readability, Summarization, Tweet Contextualization.

Abstract:

Tweet contextualization (TC) is a new issue that aims to answer questions of the form What is this tweet about?

The idea of this task was imagined as an extension of a previous area called multi-document summarization

(MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary

should ideally contain most relevant information of the topic that is being discussed in the source texts (for

MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent,

i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in

order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and

coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to

reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-

Induced Topic Search) algorithm to make up a coherent context.

1 INTRODUCTION

With the diffusion of social networks such as Face-

book or YouTube, social media have become one of

the most popular Internet services in the world. Such

sites offer today’s youth a portal for entertainment and

communication and have grown exponentially in re-

cent years. Twitter stands actually as the most popu-

lar micro-blogging service (Duggan et al., 2015). It

allows its users to communicate with short messages

known as tweets, limited by a maximum number of

characters that does not exceed 280, often in real time

and from a mobile phone. But this type of messages

generates a large amounts of data and are sometimes

non understandable for a reader because of their lim-

ited size. Since they must be written respecting this

limitation, a particular vocabulary is used and provid-

ing additional informations seems to be necessary to

understand tweet’s context without time consuming.

In tweet contextualization, a context is a summary

related to the tweet and that does not exceed 500

words. This summary should be informative and co-

herent (readable). Given a tweet, and a set of relevant

documents, the task of producing an informative and

coherent summary of those documents in response to

this tweet has attracted a great deal of attention re-

cently. However, the problem of organizing informa-

tion for contextualization so that the generated sum-

mary is coherent has received relatively little atten-

tion.

In this paper, we propose an approach for tweet

contextualization based on semantic and coherence

between sentences: our objective is to select most rel-

evant and coherent information in relation with the

tweet, extract the most important ones to provide in-

formativeness excerpt and reorder phrases to guaran-

tee coherence and readability. In this respect, the fol-

lowing questions arise: how to select the most impor-

tant phrases coming from many documents and that

deal with topics expressed in the tweet? How to en-

hance the order of selected sentences to be able to ob-

tain a readable and coherent context?

This paper has been organized as follows. Sec-

tion 2 cites some related works. Section 3 presents

our motivation and the architecture of our model. Sec-

tion 4 discusses relevant sentences extraction from a

document. Section 5 develops the idea of sentence

aggregation based on cliques detection. Section 6 de-

tails the proposed approach for sentence reordering to

improve readability for a given context. Section 7 de-

scribes our experimental results. The conclusion and

future work are presented in Section 8.

Dhokar, A., Hlaoua, L. and Ben Romdhane, L.

Improving Readability for Tweet Contextualization using Bipartite Graphs.

DOI: 10.5220/0007313900600068

In Proceedings of the 11th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2019), pages 60-68

ISBN: 978-989-758-350-6

2 RELATED WORK

In tweet contextualization, an excerpt provided from

many documents must be informative and readable.

Hence it should be grammatically correct. Coher-

ence is an essential characteristic in order to pro-

duce comprehensible texts. This aspect has received

relatively little attention in tweet contextualization.

Hence, the paper focused on document summariza-

tion to deal with this problem. In this context, several

approaches have been proposed. Barsilay and Lap-

ata (Barzilay and Elhadad, 2002) proposed to study

the proprieties of ordering information in the news

genre. They develop a method that combines con-

straints from chronological order of events and topi-

cal relatedness to improve order of sentences in multi-

document news summarization. In (Barzilay and Lee,

2004) another method is presented: it is an unsuper-

vised model that focus on text organization in a par-

ticular domain. An adaptation of algorithms for Hid-

den Markov Models is used to capture topic shift in a

text, where topics are represented by hidden states and

sentences are observations. In (Barzilay and Lapata,

2008) authors proposed a method based on entities to

assess and improve textual coherence. Their model

is inspired by Centering Theory (Grosz et al., 1995)

which supposes that adjacent sentences are coherent

if they share the same entities: authors created an en-

tity grid model to capture discourse transitions at the

sentence-to-sentence level. Following the same spirit,

Soricut and Marcu (Soricut and Marcu, 2006) and El-

sner et al.(Elsner et al., 2007) proposed a combination

between the entity-based and HMM-based models to

improve information ordering task. In 2011, Lin et al.

(Lin et al., 2011) proposed an approach of text order-

ing using discourse relation transitions. their method

is to transform the discourse relation into a discourse

role matrix that represents term occurrences with its

discourse roles in the text units (sentences). To dis-

tinguish coherence from incoherence, n-gram sub-

sequences of transitions per term in the discourse role

matrix are used. In (Guinaudeau and Strube, 2013),

authors modeled the text into a graph of sentences by

using a bipartite graph. They suppose that one set of

nodes represents entities and the other set represents

sentences of a document. Their work is based on the

fact of using a one mode projection on sentence nodes

(Newman, 2011), and then the average out degree of

sentence nodes is computed to determine how coher-

ent a document is. This method takes into account

the number of shared entities between sentences and

their grammatical functions. In (Parveen and Strube,

2015), authors proposed a graph-based method for

extractive single document summarization to improve

and evaluate local coherence for scientiﬁc articles.

They combine ILP (integer linear programming) and

a graph-based ranking algorithm to reorder and opti-

mize sentence ranking on the basis of importance. In

(Parveen et al., ), authors deal with single-document

summarization based on weighted graphical represen-

tation of the document where one set of nodes cor-

responds to topics. The Latent Dirichlet Allocation

(LDA) is used for topic modeling and to measure the

semantic relatedness between words and the topical

coherence of a given document. In (Li and Hovy,

2014), authors proposed an approach that learns a

syntactico-semantic representation for sentences au-

tomatically,using either recurrent or recursive neural

networks. The proposed architecture obviated the

need for feature engineering, and learns sentence rep-

resentations, which are to some extent able to cap-

ture the rules governing coherent sentence structure.

In (Ermakova, 2016) the author proposed three com-

pletely automatic approaches for sentence order as-

sessment where the similarity between adjacent sen-

tences is used as a measure of text coherence. Her

method is based on graph model, where the vertices

correspond to sentences and the edges represent the

similarity measure between them. She exploited the

similarities of terms, nouns and named entities. Re-

cently, authors in (Ermakova et al., 2017) presented

a self-sufﬁcient metric for sentence ordering assess-

ment based on text topic-comment structure of a text

that requires only shallow parsing. they proposed a

metric that considers the pairwise term similarities of

the topics and the comments of the adjacent sentences

in a text since word repetition is one of the formal

signs of text coherence (Barzilay and Elhadad, 2002).

Methods using bipartite graph in document sum-

marization had encouraging results in ordering task.

Inspired by the above approaches, in this paper,

a method for improving readability for tweet con-

textualization combining cliques detection, bipartite

graphs and a ranking algorithm is proposed.

3 MOTIVATION AND

ARCHITECTURE OF THE

MODEL

In tweet contextualization, a context is a summary re-

lated to the tweet and that does not exceed 500 words.

Indeed, the main objective of a TC system is to en-

hance the readability of a given tweet (that acts as a

query), to identify a list of potential topic-related re-

sources (documents) that we attempt to summarize.

The process of tweet contextualization can be di-

Improving Readability for Tweet Contextualization using Bipartite Graphs

vided into three sub-tasks: tweet Analysis, passage

and/or XML elements retrieval and construction of

the answer (context). Respecting this process, the

proposed method for tweet contextualization is pre-

sented in Figure. 1. It involves the following three

steps: tweet analysis that aims to clean tweets and

eliminate unnecessary symbols such as #, @...and

URLs, passages/XML documents retrieval where

cleaned query is transmitted to the search engine

to determine the most relevant articles to the tweet

and tweet contextualization that aims to extract

then reorder most relevant sentences related to the

tweet. Top-ranked phrases are selected to form con-

text (within the limit of 500 word).

The main objective of this work is to guarantee in-

formativeness and coherence between different parts

of the context to be able to construct an appropri-

ate summary. In view of importance of both rele-

vance and coherence in a contextualization system,

we have considered that this work must combine these

two aspects to achieve good performances in infor-

mativeness and readability. Hence, it is interesting

to consider that a sentence included in the ﬁnal con-

text should be relevant regarding the query, informa-

tive and coherent with other sentences. The proposed

method is divided into two main modules: the ﬁrst

one aims to enhance the informativeness by select-

ing the most relevant sentences from many documents

considering some measures, and the second module

aims to improve the readability of the contextualiza-

tion system by reordering selected sentences to ensure

higher degree of coherence to the constructed context.

This approach will be detailed in next sections.

4 RELEVANT SENTENCE

EXTRACTION FROM A

DOCUMENT

The goal of this part of the proposed system is to

extract the most relevant phrases from the most

relevant documents. For that, this step into two

sub-tasks: document ﬁltering regarding the tweet and

sentence scoring.

In document ﬁltering regarding the tweet, our ob-

jective is to choose most informative sentences that

deal with topics expressed by the tweet. Hence

we opted for ﬁltering the relevant document by

keeping only sentences that are correlated to the

query. This can be easily done by calculating the

cosine similarity between the tweet and the can-

didate sentences given by the following formula:

Similarity(Q, S) =

∑

i=1

Freq(q

, Q)

∑

i=1

(Freq(q

, Q))

∑

i=1

Freq(s

, S)

∑

i=1

(Freq(s

, S))

(1)

Where Q=q

,...,q

is a query, S=s

,...,s

is a

sentence, Freq(q

,Q) is the occurrence of the i-th to-

ken in a query and Freq(s

,S) is the occurrence of the

i-th token in a sentence. If the token is not presented

in the query or in the sentence, q

(resp.s

) is equal to

0 respectively.

In every ﬁltered document, sentences do not have

the same importance in term of relevancy according

to the tweet. For that, we propose to make the dif-

ference between most relevant phrases and less ones

(Dhokar et al., 2017): for each candidate sentence, a

score is computed. This score takes into account the

relevance of the sentence compared to the title of the

document and the importance of the sentence in its

original document compared to other sentences in the

same article. Best scored sentences are selected and

the score of each phrase is given by:

= Similarity(T, S

) + Imp(S

) (2)

Where Sp

is the associated score of a sentence S

Similarity(T,S

) is the similarity estimated between

a sentence S

and the title of the document T and

Imp(S

) is the score that estimates the importance of a

sentence (S

) in a document. The similarity between

a sentence S

and the title of the document T is calcu-

lated using the following equation:

Similarity(T, S) =

∑

i=1

Freq(t

, T )

∑

i=1

(Freq(t

, T ))

∑

i=1

Freq(s

, S)

∑

i=1

(Freq(s

, S))

(3)

Where T=t

,...,t

is the title of the corresponding

document, S=s

,...,s

is a sentence, Freq(t

,T) is the

occurrence of the i-th token in a title and Freq(s

,S) is

the occurrence of the i-th token in a sentence. If the

token is not present in the title or in the sentence, q

(resp.s

) is equal to 0 respectively.

The importance of a sentence (S

) in a document is

calculated until divergence and given by (Brin and

Page, 2012):

Imp(S

) = (1 − d) + d

∑

∈Neighbors(S

)

Sim(S

, S

)

∑

∈Neighbors(S

)

Sim(S

, S

)

× Imp(S

) (4)

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

Figure 1: The proposed contextualization model.

Where d is a dumping factor (usually set to 0.85),

Neighbors S

is the set of sentences connected with

and Sim(S

) is the similarity score between sen-

tences S

and S

and given by (Mihalcea, 2004):

Sim(S

, S

) =

∑

m∈S

f req(m, S

) + f req(m, S

)

log|S

| + log|S

(5)

Where , freq(m,S

) is the occurrence of a word m in a

sentence S

, respectively S

and log |S

| is the length

of a sentence S

, respectively S

5 SENTENCE AGGREGATION

BASED ON CLIQUES

DETECTION (SACD)

As mentioned in the previous section, we work with n

top documents from the search phase. From each doc-

ument, best scored sentences are selected and aggre-

gated together. However, a good context should have

a good quality respecting two fundamental aspects of

a contextualization system: relevance and coherence.

We therefore propose to reﬁne the choice of relevant

and coherent sentences to include in the context. In

this respect, it is proposed to model sentences in a

graph then use cliques detection to select most coher-

ent groups of phrases. Our hypothesis is that a set

of sentences belonging to the same clique can form a

coherent and semantically linked passages. Usually,

each node in a clique is, in some way, highly related

to every other node. This characteristic makes clique

identiﬁcation a very important approach to uncover

meaningful groups of sentences from a graph. In this

work, we opted for ﬁnding maximal cliques of a graph

to identify coherent sentences in order to produce a

readable context (Dhokar et al., 2017).

5.1 Cliques Computation

In the literature, many pieces of work have been pro-

posed to model a set of sentences by a graph in order

to obtain links between phrases and passages (Salton

et al., 1997),(Yeh et al., 2008). In this work, we try to

adapt the same concept in order to model a group of

phrases (here aggregated sentences resulted from the

ﬁrst step of the proposed system) as a graph, in order

to obtain a network of sentences that are related to

each others, resulting in a sentence similarity graph.

This graph is composed of nodes and edges linking

nodes and each node represents a sentence. A connec-

tion between two nodes exists if and only if they are

similar with respect to a similarity threshold α. The

degree of connection between two sentences S

and S

is measured by the formula used in equation 5. Our

approach to identify cliques is based on the notion of

a maximal clique. A maximal clique of a graph G is a

clique that cannot be extended by including one more

adjacent vertex (Regneri, 2007). Cliques are allowed

to overlap, which means that sentences can be mem-

bers of more than one clique. The purpose of this step

of this work is to detect all maximal cliques present

in the graph using Tomita algorithm (Tomita et al.,

2011).

5.2 Cliques Selection

In tweet contextualization, a context is a summary

related to the tweet, containing coherent and related

groups of sentences and that does not exceed 500

Improving Readability for Tweet Contextualization using Bipartite Graphs

words. Respecting this constraint, the aim of this

step is to propose a method for cliques selection to

determine cliques to be considered in the ﬁnal con-

text. Hence the proposed method is depicted in ﬁg-

ure 2. A set of cliques (resulted from the previous

Figure 2: Our proposed method for cliques selection.

step) is considered as an input of the algorithm. then,

the clique which has the highest size N is chosen to be

included in the ﬁnal context. If the limited size of the

excerpt (500 words) is achieved, we consider only the

selected clique. If not (we didn’t achieve 500 words),

the clique which has the size N-1 is added. The oper-

ation of cliques selection is repeated until having 500

words in the ﬁnal context. After merging all selected

cliques, redundant sentences are eliminated.

6 SENTENCE REORDERING

6.1 Motivation

This section highlights the proposed approach for sen-

tence reordering in order to generate a readable con-

text for a given tweet. According to the previous

step, the obtained context is composed of a set of

cliques. The proposed method is divided into two

steps: the ﬁrst step aims to reorder sentences into

the same clique and the second step aims to reorder

cliques between them.

In this work, we focus on the ﬁrst step and our goal

is to propose a method to reorder sentences into the

same clique. The intuition we have behind this idea is

that sentences belonging to the same clique are corre-

lated and treat the same themes. In this respect, their

reorganization remains indispensable in order to have

a coherent and grammatically compact passage (para-

graph). This section describes the adopted technique

to improve readability for tweet contextualization. In

this context, we model the text using a graphical rep-

resentation and then apply HITS algorithm to reorder

sentences belonging to the same clique. A graph can

easily capture the essence of the whole text without

leading to high computational complexity and a rank-

ing algorithm (here is HITS algorithm) takes into ac-

count global information to calculate the rank of a

corresponding node (sentence). These two character-

istics can help us to improve readability and coher-

ence to a given context. In this work we opted to

combine a bipartite graph and HITS algorithm. We

start by introducing the graphical representation of

a clique, followed by a description of the algorithm

used to quantify the importance of phrases in a clique

and reorder considered sentences.

6.2 Proposed Method for Sentences

Reordering

6.2.1 Graphical Representation of a Clique

Considering a graphical representation of a text is not

recent. It is adopted by various approaches (Mihal-

cea and Tarau, 2004; Radev et al., 2004). In 2014,

Parveen and Strube proposed to use a bipartite graph

representation of text based on the entity grid repre-

sentation proposed by Barzilay and Lapata (Barzilay

and Lapata, 2008). Employing this type of represen-

tation can help us to determinate the importance of

sentences and to detect correlations between phrases.

A bipartite graph is an unweighted graph G=(V

,L),

containing two sets of nodes, where V

is the set of

sentences, V

is the set of most expressive words (

here we will consider two types of expressive words:

named entities and most frequent words) and L is the

set of edges. An edge exits between a sentence and

an expressive word only if the word is mentioned in

a sentence; and there are no edges between nodes of

the same set. Figures 3, 4 and 5 show an example of a

text, its associated entity grid and its bipartite graph.

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

Figure 3: An example of a text.

Figure 4: Entity grid of the model summary from Figure 3.

Figure 5: Bipartite graph derived from the entity grid from

Figure 4.

6.2.2 Using HITS Algorithm for Sentence

Reordering

The aim of this step is to reorder sentences in the same

clique to improve readability in the context. Hence,

coherence improvement is presented as a ranking

problem. Inspired by (Parveen et al., 2015), it is pro-

posed to use HITS algorithm to calculate the rank

of a sentence in a clique. This algorithm considers

two types of nodes: hub nodes and authority nodes.

Since our graph is a bipartite graph, we can consider

sentences as authority nodes and words as hub nodes

(Kleinberg, 1999), to reorder sentences in the associ-

ated bipartite graph. For every sentence in the graph,

the importance of the phrase should be calculated in

two steps:

1. Calculate the hub score of a node (word) using the

following formula:

HubScore = A.AuthorityScore (6)

Where A is an adjacency matrix which represents

the connection between nodes in a graph. Each

node’s hub score has to be updated and the asso-

ciated score is equal to the sum of authority scores

of each node that it points to.

2. Calculate the authority score of a node (sentence)

using the following formula:

AuthorityScore = A

.HubScore (7)

Hence the authority weight is high if it is pointed

at by a hub having high weights.

The rules given by equations 3 and 4 are applied

until convergence (values of authorities and hubs are

stable). Also, all nodes in the graph have to be initial-

ized. Initial rank of a sentence is given by the follow-

ing formula:

ItitialRank(S

) = 1 + similarity(S

, Tweet) (8)

Here similarity(S

, Tweet) is the cosine similarity be-

tween a sentence S

and a tweet. We consider that ini-

tial importance of a sentence S

is related to the tweet.

Initial rank of a word is given by:

InitialRank(w

) = 1 + t f (w

, C)

+ occurence(w

, tweet) (9)

Here t f (w

, C) is the term frequency of w

in a clique

C and occurence(w

, tweet) indicates the occurrence

of w

in a tweet. Hence if w

is not present in the

tweet then occurence(w

, tweet) = 0. If it is present

then occurence(w

, tweet) = 1. We consider that ini-

tial importance of a word depends on the clique and

the tweet.

7 EXPERIMENTAL RESULTS

This section highlights experimental results given by

the proposed contextualization system. Obtained re-

sults are compared with results provided by INEX

2014. Before reporting experimental results, it is es-

sential to indicate the Test Data and the evaluation cri-

teria that we will consider.

Improving Readability for Tweet Contextualization using Bipartite Graphs

7.1 Description of the Test Data

In this study, we use the collection of articles and

tweets made available by INEX . The corpus has been

rebuilt in 2013 from a dump of the English Wikipedia

from November 2012. All notes and bibliographic

references were removed to facilitate the extraction of

plain text answers. It is composed of 3 902 346 arti-

cles and 240 tweets selected from the CLEF RepLab

(Amig

o et al., 2013) 2013 to build the 2014 INEX

collection. 70 tweets were considered for evaluation.

7.2 Evaluation Measures

Contexts are evaluated according to readability and

informativeness (Bellot et al., 2013). Readability

aims at measuring how clear and easy it is to under-

stand the summary and is manually evaluated. How-

ever, informativeness aims at measuring how well the

summary explains the tweet or how well the summary

helps a user to understand the tweet content (Bellot

et al., 2013).

7.2.1 Informativeness

This criteria calculates the dissimilarity between a ref-

erence text and the proposed summary (Bellot et al.,

2013), (SanJuan et al., 2012). It is a measure that

varies between 0 and 1, and the lower this dissimilar-

ity, the more the proposed summary is similar to the

reference text. It is given by:

Dis(T, S) =

∑

t∈T

(P − 1)



1 −

min(log(P), log(Q))

max(log(P), log(Q))



Where: P =

(t)

+ 1 and Q =

(t)

+ 1

T, is a set of query terms present in a reference sum-

mary and for each t ∈ T , f

(t), the frequency of a

term t in a reference summary, S, a set of query terms

present in a submitted summary and for each t ∈ S

and f

(t), the frequency of term t in a submitted sum-

mary. T may takes three forms: unigrams made of

single lemmas, bigrams made of pairs of consecutive

lemmas (in the same sentence) and bigrams with 2-

gaps also made of pairs of consecutive lemmas but

allowing the insertion between them of a maximum

of two lemmas.

7.2.2 Readability

In this evaluation the assessor indicates where he

misses the point of the answers because of highly in-

coherent grammatical structures, unsolved anaphora,

or redundant passages. Each summary consists in a

set of passages and for each passage, assessors had to

tick four kinds of check boxes. The guideline was the

following(Bellot et al., 2013): syntax (S) box is ticked

if the passage contains a syntactic problem (bad seg-

mentation for example), anaphora (A) box is ticked

if the passage contains an unsolved anaphora, redun-

dancy (R) box is ticked if the passage contains a re-

dundant information, and trash (T) box is ticked if the

passage does not make any sense in its context.

To evaluate summary’s readability, three metrics

are used based on relevancy or relaxed metric where

a passage is considered as valid if the T box has not

been ticked, syntax metric where a passage is consid-

ered as valid if the T or S boxes have not been ticked

and structure or strict metric where a passage is con-

sidered as valid if no box has been ticked.

7.3 Results and Disscussion

This section highlights experimental results of the

proposed method. Hence we can evaluate our system

according to informativeness and readability.

7.3.1 Informativeness Evaluation

To evaluate informativeness for tweet contextualiza-

tion, we conducted a simulation, namely run-1, in

which we evaluate our system considering the pro-

posed method of cliques selection proposed in sec-

tion 5.2. We have compared our runs with the follow-

ing different runs submitted by INEX 2014 partici-

pants: in Best-run, participants (Zingla et al., 2014)

used mining association rules between terms and in

REG-run, participants (Torres-Moreno, 2014) used

an automatic greedy summarizer named REG (RE-

sumeur Glouton) which uses graph methods to spot

the most important sentences in the document. The

proposed method has ameliorate our informativeness

results. We can note that our proposed approach

gives encouraging informativeness results compared

to other systems proposed at INEX 2014 (see table

1).

Table 1: Table of informativeness results.

Run Unigram Bigram Bigrams with 2-gaps

Best-run 0.7632 0.8689 0.8702

run-1 0.8180 0.9072 0.9102

REG run 0.8731 0.9832 0.9841

7.3.2 Readability Evaluation

As mentioned in the previous section, readability is

manually evaluated. Hence, contexts readability were

provided by 77 assessors. To evaluate readability for

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

Table 2: Table of readability results.

Run Relaxed Strict Syntax

Best-Read-run 94.82 72.16 72.27

run-1 88.47 50.65 67.20

run-2 91.17 60.79 75.86

run-3 90.77 55.93 74.91

run-4 84.67 54.17 70.18

run-5 82.16 52.66 71.23

run-6 92.33 62.65 77.53

Last-Read-run 90.10 24.68 53.83

the proposed system, we considered the following

simulations: run-2 where we consider contexts with

sentence reordering in cliques using named entities,

run-3 where we consider contexts with sentence re-

ordering in cliques using most frequent words (MFW)

and the number of considered MFW is weighted ac-

cording to sentences, run-4 where we consider con-

texts with sentence reordering in cliques using most

frequent words (Here we consider that the number of

considered MFW is equal to 10), run-5 in which we

consider contexts with sentence reordering in cliques

using most frequent words and the considered num-

ber of MFW is higher then 10, and run-6 in which we

consider overlapping words between named entities

and most frequent words. We have compared our runs

with the following different runs submitted by INEX

2014 participants: Best-Read-run which is the best

readability run and Last-Read-run which is the last

readability run. Simulation results are summarized in

table 2.

By observing the results given in table 2, we can

note that there is an interesting improvement for the

readability results considering the used three metrics:

comparing run-1 (without sentence reordering) and

run-2 (with sentence reordering) we can conﬁrm that

sentence reordering based on combining HITS algo-

rithm and named entities is efﬁcient to ameliorate the

readability of our system. We can also see that us-

ing most frequent words in sentence reordering im-

proves readability results, in particular in run-3 com-

pared with run-4 and run-5, what conﬁrms the inter-

est of sentence reordering using bipartite graphs. An

other important observation is that the provided re-

sults from run-2 and run-3 are very close. this can

be explained by the fact that the set of most fre-

quent words and the set of named entities are over-

lapping.i.e, many words are in common between the

two sets. For that we proposed an other run namely

run-6 where we consider overlapping words. Accord-

ing to the Table 2, we can see that using overlap-

ping words between named entities and most frequent

words in sentence reordering improves readability re-

sults. Comparing our results with ofﬁcial results given

by INEX 2014, we can see that our proposition gives

encouraging results that are in line with the values ob-

tained at INEX 2014.

8 CONCLUSION

In this paper we focus on the problem of readability of

tweet contextualization. Our main contribution is the

proposition of a method based on the use of bipartite

graphs to reorder sentences in cliques and to improve

readability for Tweet Contextualization. We opted to

combine HITS algorithm and two types of words in

the graph: most frequent words and named entities to

make up coherent contexts. These proposals and their

formal studies are complemented by an experimen-

tal study to compare them with results from INEX.

We found that our model obtains encouraging results

since that our returns are in line with the results of

the INEX 2014 and it is essential to point out that our

system proposed a compromise between informative-

ness and readability. We propose in our future work

to make an order for cliques (paragraphs) in the con-

text to improve the quality of the context with respect

to informativeness and readability.

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