DEVELOPMENT OF TOOL TO PROMOTE WEB ACCESSIBILITY FOR DEAF

Dhinaharan Nagamalai et al. (Eds) : ITCCMA, CSITY, AIFZ, NWCOM, SIGPRO - 2017
pp. 01– 15, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70701
DEVELOPMENT OF TOOL TO PROMOTE
WEB ACCESSIBILITY FOR DEAF
Ahmad A. Alhamed and Maha S. Alqhtani
Department of Information Systems, King Saud University, Riyadh, KSA
ABSTRACT
This paper presents an intelligent tool to summarize Arabic articles and translate the summary
to Arabic sign language based on avatar technology. This tool was designed for improving web
accessibility and help deaf people for acquiring more benefits. Of the most important problems
in this task, is that the deaf people were facing many difficulties in reading online articles
because of their limited reading skills. To solve this problem the proposed tool includes a
summarizer that summaries the articles, so deaf people will read the summary instead of
reading the whole article .Also the tool use avatar technology to translate the summary to sing
language.
KEYWORDS
Accessibility, Arabic sign langauge, Key terms summarizer, Avatar.
1. INTRODUCTION
The world wide web has evolved very fast in recent years and become necessary for everybody. It
is a rich source of information and documents in all fields. Unfortunately, deaf people cannot take
full advantage of these advances in technology. Some Web sites are not sufficiently accessible to
deaf people, making it difficult or impossible for them to contribute to the Web.
Web accessibility means that the web is accessible to every body even to people with disabilities.
In another word, people with disabilities can perceive, understand, and interact with the Web.
There are many attempts to improve the web accessibility by providing closed captioning and
transcriptions, whenever a sound appears. In addition, the accessibility for deaf can be improved
with the utilization of the sign language.
Deaf people face problems in reading electronic documents or articles that are available on
certain web pages. They have a hard time to understand the written language because of their
limited vocabulary and language skills. The poor language capability for deaf people is because
they cannot listen and practice the basic language techniques[45]. According to the previous
studies in the field of the deaf, reading level of the deaf is lower than the reading level of hearing
people. Furthermore, the level of experience achieved by deaf children in four years is equivalent
to the level of one year for hearing children [24].

2 Computer Science & Information Technology (CS & IT)
Most web pages offer written information such as news, articles or other documents. These web
pages may lose a number of visitors who are deaf. Because deaf readers have difficulties with
many skills that are required for reading such as word identification , vocabulary .Also they have
problems with reader-based skills like working memory [16].With increased advancement of
technology text summarization has a number of applications that are used in the analysis of a
high volume text documents. With text summarization tools the online documents can be
summarized into a shorter form .So, deaf can read only the summary instead of reading the whole
document. Automatic text summarization tools can be helpful for deaf people by reducing the
time and efforts needed to read and understand the documents .Moreover, many deaf people
follow sign language which is a lot easier than the written word. Web accessibility can be
improved be integrating text summarization tools with sign language to provide better access for
the deaf.
This paper aims to develop a new tool to summarize the online articles in order to produce a
summary that contains the main points of the original articles. And interpretation of these
summarized articles to sign language by using 3D avatar technology.
The proposed tool will be responsible for two major tasks. The first task is to build Automatic
Arabic text summarization tool to summarize certain Arabic articles to create shorter versions of
these articles. There are two types of summarization extractive and abstractive. Extractive by
selecting important pieces from the original documents to produce shorter form. An Abstractive
summarization generates summaries whose material is not presented in the input text by
understanding of the main concepts in a document and then use new words that are not present in
the original document to express these concepts. Abstraction methods are still difficult to achieve
because it requires text analysis, modeling, and language generation techniques [40].
There are different methods to implement extractive summaries. The most important ones are: the
linear methods that give a score for each sentence depending on heuristic measures, such as Naive
Bayesian classifier, Latent Semantic Analysis (LSA) which is inspired by latent semantic
indexing .And a Graph Based methods that represent the sentences in the text as nodes with a
directed graph; the two sentences are connected if its share some words may its refer to some
similarity [40].
The second task of the proposed tool is to translate the output summary form the previous step to
Arabic sign language using avatars technology. Deaf people use sign language as their first
language to communicate with others [10]. Sign language relies on facial expressions and body
language to convey meaning, rather than transferred to the acoustically sound patterns.
The proposed tool will include a dictionary of the most used words in Arabic with their
corresponding sign files. Visualization of signs by using computers have been evolved from
pictures to video clips and finally to 3D Avatar. We use 3D Avatar because sign files are very
smaller than videos and pictures; thus the amount of storage space required is minimal. Also, the
possibility of moving the 3D avatar at different angles making it easy for the deaf to understand
the signal [8].
The main objective of this paper is to improve web accessibility for deaf people by developing a
tool facilitating communication through the Web for deaf. This tool will summarize Arabic
articles and translate the output summary to sign language. Thus will help deaf people to convey
the meaning of these articles in less time and efforts.

Computer Science & Information Technology (CS & IT) 3
2. RELATED WORK
This part presents a review of previous studies related to web accessibility for deaf people,
automatic Text summarization and translating Arabic Text to ArSL:
2.1 Web Accessibility
An attempt in [27] for making e-learning available to persons with disabilities. It presented a Web
accessibility framework for the Arab disabled users and facilitated their learning. The proposed
system depends on extracting the website content via reading; then the derived content is
presented in ArSL animations for the deaf users or speech for the blind users. In order to translate
the extracted material the system use ArSL animated library which consists of each Arabic word
and the corresponding ArSL animated GIFs. This system implemented in PHP language and
MySQL. Improving web accessibility for the deaf people is relatively possible and inexpensive.
The best solution is the use of closed captioning and transcriptions, whenever a sound appears.
Web accessibility can also be improved with the utilization of the sign language. Most people
who are deaf understand and use the sign language which is easier than the written word.
Unfortunately, implementation of sign language is expensive because it requires a sign
interpreter. In order to help the deaf user to understand the audio material. Some websites involve
captioning within the video or provide a link located near the video or audio material to access
the captions and transcriptions.
2.2 Automatic Text Summarization:
R. Dixit and S.Apte [37] proposed a method for text summarization based on fuzzy logic to
extract important sentences based on their features as a summary. The fuzzy logic system
includes four components: fuzzifier, defuzzifier ,an inference engine, and the fuzzy knowledge
base. In the fuzzifer, inputs are translated into linguistic values by using a membership function.
Then, the inference engine uses fuzzy IF-THEN rules to derive the linguistic values. In the final
step, the output variables from the inference engine are converted to the final values by the
defuzzfier using a membership function for representing the final sentence score. Then all the
sentences of a text are ranked in a descending order according to their scores. Sentences that have
the highest score are extracted as a text summary. G. Al_Gaphari et al.[16] presented and
discussed a graph based centrality algorithm for automatic Arabic text summarization. The graph-
based algorithm extracts the most important sentences in a document by computing the similarity
between two sentences and assessing the centrality of each sentence in a cluster. The researchers
use a real-world dataset for testing and validating the software summarizer performance, and the
result was very promising. They concluded that the proposed summarizer may outperforms other
summarizers used for text summarization. Sobh et al. [40] Integrates Naive Bayesian and Genetic
Programming (GP) classification methods in an optimized way to extract the summary. The
process of automatic summarization is becoming very important with an increased number of
information sources available on the web. The proposed system use Naive Bayesian classifier and
Genetic Programming classifier in parallel. Naive Bayesian Classifier classifies each sentence
based on its features such as Sentence length, position in the paragraph, or Sentence similarity.
Genetic Programming Classifier is automated learning of computer programs. GP was used in
many fields, for example, financial market, image processing, signal processing, and pattern
recognition. Authors choose to use the Discipulus1 GP system which is the world’s first and
fastest Genetic Programming system that writes computer programs automatically in Java, C, and

Intel assembler code. They used the downloadable free version with default settings for cross-
over when running the tool for classification. Authors concluded that by integrating both
classifiers, the union for integration increases the result summary and intersection for integration
decreases the summary size. Finally, the proposed system is optimized and able to produce
summaries comparable to human-generated summaries.
Many systems have been investigated in the area of automatic text summarization, and some of
these are available online, and others are used as commercial software. In this section, we will list
some of these systems:
SweSum: It is automatic text summarizer for Swedish by Martin Hassel and Hercules
Dalianis. It summarizes Swedish news in HTML/text format on the web. This system
calculates the frequency of the keywords, in which sentences they appeared, based on
statistical and heuristic methods .
WebSummarizer: It is an application to summarize web pages and documents to produce
interactive outlines and Visual Summaries. It also offers summarization of Wikipedia
articles and supports many languages such as German ,French and Spanish.
TextCompactor: This summarizer calculates the frequency of each word in the text. Then,
each sentence score is calculated based on the frequency count of the words it contains.
The most important sentence is considered to be a sentence with the highest frequency
count.
2.2.1 Arabic Text Summarization Systems:
The Summarizer of AramediA: This Summarizer used for summarizing Arabic and
English texts. It extracts the important sentences based on linguistic analysis of the input
text. The user can preview these ideas instead of reading the whole document .
Sakhr Summarizer: Sakhr is a commercial online Arabic text summarization system. This
Summarizer identifies the most relevant sentences within a text based on a set of text-
mining tools. The Summarization engine applies the Sakhr Corrector to correct the input
Arabic text automatically from the common Arabic errors, and the Keywords Extractor to
identify a list of important keywords that to identify the important sentences [40].
SARA© : Is an automatic Arabic text summarization developed by RDI ( Research &
Development International). It extracts the most important sentences based on the
morphological analysis of the words and mathematical analysis of the sentences and their
relationships. The user of the system can easily control the size of the summary (e.g.,
20% or 50%, and so on).And the system is valid for all types of texts such as news,
science, arts, sports, etc.
2.3 Text To Sign Language
Wraya, et. al, [4] presented a system designed to support transactions between a post office clerk
and a deaf customer. This interactive translates the clerk’s speech into British Sign Language
(BSL) and displays the signs on the screen, using a specially-developed avatar. However, the

system is limited to post office operations. The system operates through a series of stages, start
when a deaf person enters the post office and requests the service using gestures. Once the clerk
know that the customer is deaf he/she start speaks into his/her headset microphone. Then the
automatic speech recognizer selects possible messages from an extensive set of pre-entered
targets and displays those with the highest probability on a screen. The clerk confirms the
message intended. Then the message contents, are translated into a set of sign instructions and
displayed on a video screen. The authors recognized that the system potentially offers access to a
range of information that they may previously have had difficulty obtaining.. The developed
system achieved an accuracy of identification of the signed phrases of 61% for complete phrases
and 81% for sign units. Email is a convenient communication tool for most of the people.
Unfortunately, deaf people cannot take the full advantages of these techniques. Non-deaf people
are easy to use Email to communicate with their family, friends or other people. However, deaf
people have difficulties to study the nature language. They have a hard time to write a proper
sentence or even a short mail because they have limited vocabulary capabilities and weak
language skills. When they read a sentence, they do not understand the whole sentence properly.
Authors proposed a method to convert Chinese texts to a speech and sign language
simultaneously. With this proposed system, a deaf people can easily read an Email or articles by a
translated sign language stream [45]. M. JEMNI and O. ELGHOUL, [26] Developed a Web-
based interpreter of Sign Language (SL). This tool facilitates the communication between hearing
people who do not know sign language and deaf individuals. Authors discussed three main
approaches: • Writing based systems The first transcription of signs languages based on writing
or drawing symbols. Many systems of transcription appeared like HamNoSys and SignWriting.
These systems used to encode sign language in a linear way.
2.3.1 Video-Based Systems
The video-based systems are a video sequence corresponding to a human interpreter. This video
sequence has many limitations in performance, the contents are not easily reusable, and therefore
a human translation in a language of signs is necessary and remains very costly [26].
2.3.2 Avatar based systems
Avatar based systems this new technological context, involve the creation of a virtual character.
Avatar is more precisely and more interested in communication systems in sign language by
animation of virtual 3D persons. The proposed tool is called WebSign it based on the technology
of avatar. The input of the tool is a text in natural language, and the output is a real-time
interpretation in sign language. The tool uses a dictionary of word and signs to perform the
interpretation. The first version of Web Sign tool is finalized, and the authors attempt to contacts
with specialized deaf associations to train users and evaluate the system [26].
M. Mohandes [28] discussed the importance of Sign Language for deaf people as their natural
way of communication just like a speech for non-deaf people. Deaf people often communicate
with non-deaf people through a sign language interpreter. Sign language interpreter is not always
available when needed. Therefore, there is an increasing need to provide an automatic interpreter
for translating signs to speech and the reverse using the modern technology. He proposed a
system that converts Arabic text to Arabic sign language. The system was developed using Java
Server Pages (JSP), implicitly having all the benefits of the Java programming language. The
system has a database to store Arabic words with the corresponding signs representation. When
the word is typed a letter by letter, the system automatically starts searching for the word if the

word is available in the database then the recorded clip will be shown. If the word does not have a
corresponding sign in the dictionary, then finger spelling is done by displaying images that
representing each letter of the word. The author concluded that this proposed system can be used
to teach deaf or their families the Arabic Sign Language.
3. WEB ACCESSIBILITY AND DEAF PEOPLE
Deafness has different meanings, but those meanings are relevant in a way or another; however,
different definitions of deafness have been given; the Wikipedia, defined deafness as following:
"Hearing loss, deafness, hard of hearing, or hearing impairment is a partial or total inability to
hear. It affects the development of language for children and for adults it can cause work-related
difficulties ".
Deaf people are people who suffer from a weakness or loss of hearing. Deaf people are facing
many difficulties when communicating with their society. The main solution to help deaf people
is the use of Sign language. Sing language provides an easy way of communication with each
other's and it is the easiest way to learn for any deaf. When they want to interact with normal
people, they need a translator who understands both languages.
Deaf people use the internet, like any other non-deaf people. While the Web provides a large
amount of information and resources in all fields of knowledge, it also poses difficulties when
content is not designed to be accessible. Deaf users cannot take full advantage of the web
because of their hearing disability. Because they are deaf they cannot listen to any video; they
cannot listen to any audio. For example, if deaf users want to use YouTube they cannot listen to
any of video of audio material which causes boringness in the film. Deaf people cannot take full
advantage of the web they cannot even listen to any educational video to increase their
knowledge. Some users with a minor hearing disability can be treated and helped by using some
mechanical instruments to get them hear full sound. But such devices are expensive. Another
difficulty is that many deaf people do not have highly developed reading skills. Because they
have a limited reading vocabulary. So, the access to the information on web pages will be
difficult.
Some web pages are designed to be accessible for all users even for people with disabilities by
providing content suitable for all type of users this is called web accessibility. Web accessibility
can be defined as that people with disabilities can use the Web, i.e., can perceive, understand, and
interact with the Web. When web pages are accurately designed or made, developed and modified
then all users or clients have equal access to information and functions.
Most Web sites have accessibility obstacles that make it difficult for many people with
disabilities to use the Web sites. Here some types of web accessibility obstacles that deaf people
commonly encounter from poorly designed websites and web tools.
Lack of sign language to supplement the information and text that is difficult to read.
Web sites that offer Audio content, such as videos with voices, without captions or
transcripts.

Web sites that include captions or transcripts for the Audio or video contents but do not
present options to adjust the text size and colors for captions.
Web sites that offer services, including web applications, which rely on interaction using
voice only.
4. ARABIC SIGN LANGUAGE
Sign Language (SL) is an essential communication tool used among deaf people. Sign language is
non-voice communication used by people with special needs impaired (deaf) or audio (dumb).
Sign language relies on facial expressions and body language to convey meaning, rather than
transferred to the acoustically sound patterns [5]. Sign language primary components consist of
Manual Features (MF) and Non-Manual Features (NMFs) .Manual Features are signs that are
presented by one or both hands in different shapes,movements , orientations and locations. While
NMFs are features that do not require hands, it consist of the several facial expressions, head
tilting, mouthing, shoulder raising, , and similar signals that we add to our hand signs to create
meaning , feeling and/or represent the morphological and syntactic markers of a sentence [5].
As an independent language, Arabic sign language has its own rules. ArSL is limited to represent
nouns, verbs, and adjectives. Its sentences have particular characteristics can be illustrated as
follow:
Names: There is no exact representation of the personal name in sign language. Instead,
deaf people depend on a special mark of a person to describe that person such as a big
nose, his job …etc. Also, finger spelling is used to spell out the name of a person or any
nouns .
Verbs: are used in a simple form without specifying the time (past, present, future). Verbs
are attached with words that indicate the time of occurrence (tomorrow, yesterday,
now…etc) .
Adjectives: This is one of the most important parts of any sentence. It is used in the
Arabic language to simplify the structure of the sentences.
Plural: are represented as a singular attached with the sign of number that indicates the
quantity .For example, the word (‫ن‬ ) are expressed in sign language by two signs
one for the word (‫ب‬ ) and the other for the sign of the number two .
Prepositions and adverbs: are expressed in the context of articulation by specifying
movements, orientations, and locations [5].
5. TOOL DESIGN AND IMPLEMENTATION
In this section, we describe the design of our tool that summarize Arabic text and translate it to
Arabic sign language.

5.1 Tool Design
The proposed tool architecture is illustrated in Figure 1.The tool is divided into two main phases,
the key terms summarizer and the process of converting the output summary to sign language.
Figure 1: Proposed Tool Architecture
The process goes into several steps:
Key Terms summarizes read the text and then identify the boundaries of the sentences.
Remove stop words and extract the Key terms which is the most frequent terms in the
article.
Extract the summary by extracting only the shortest sentence between the sentences
belonging to the same key term.
Convert the extracted summary into sign language by phrasing the summary into words.
Search the signs data base for the related signs.
Display the signs in the web page as animation by the signing avatar.

5.2 Tool Implementation
The tool was implemented using PHP language to integrate the tool components. In this
subsection we will talk about our tool development phases: 1) summarizer building; 2) signs
building; 3) Database (DB) building; 3) user interface design.
5.2.1 Summarizer building
5.2.1.1 Read TXT File
In this phase we will use PHP programming language to read an Arabic file into our tool, and
then we will apply our approach on it.
5.2.1.2 Sentence Segmentation
Segmentation is the process of identifying the boundaries of Segmentation is the process of
determining the boundaries of the sentences. In Arabic language punctuation marks are used to
divide the sentences which determine the end of each sentence, such as question marks
(‫,)؟‬exclamation marks (!),commas (,),semicolons (;), colons (:), and periods (.), are chosen to split
the text into sentences.
5.2.1.3 Stop Word Removal
This algorithm will remove the Arabic stop words which consist of the following word
categories: Adverbs, Prepositions, Pronouns, Conditional Pronouns, Interrogative Pronouns,
Referral Names/ Determiners, Relative Pronouns, Transformers (verbs, letters), Verbal Pronouns
.
5.2.1.4 Key terms extraction
The algorithm will extract the Key term which is the most frequent term in the text, by scanning
the text for the terms and calculate the key terms frequency in the original text by applying this
equation:
Key term rank = Σ (Key term Frequency)
Only one sentence of the sentences that are belonging to one Key term will be extracted .In order
to avoid redundant selection of the sentences the length for each sentence will be calculated and
give a rank for each sentence, then extract only the shortest sentence between the sentences
belonging to the same Key term to be in the output summary. The length of sentence will be
calculated using this formula :
Length Of Sentence = Σ (Count Of All Terms In Sentence)
5.2.2 Algorithm
For our tool we will use the following algorithm:

1. Load text file
2. Make preprocessing phases:
2.1 Sentences segmentation
2.2 Stop word removal
3. Extract and rank Key terms
(The most frequent terms in the text)
4. Extract the summary
(The sentences which are referring to one Key terms)
If (two sentences belong to the same Key term)
- Calculate the length of every sentence
(Number of terms in the sentence)
- Extract the sentence with the shortest length
5- Display the summary.
5.2.3 Signs building
For the representation of signs we used a 3D animated virtual agent, the avatar (VGuido)
developed by Televirtual Ltd. Also we used HamNoSys to generate the animations from the
descriptions of signs. HamNoSys is a system for writing signed languages was developed by
Hamburg University. Since computers cannot process the symbols of HamNoSys, SiGML, was
used to translate HamNoSys symbols into SiGML which is a modified version of XML. This
information is processed in a synthesizer, which, taking into account the description of the
geometry of the animated agent. Finally, the description of the signs is represented by the virtual
avatar .
Also we use eSign software which is an Editor tool, designed in the project eSign provides an
editing environment .In eSign editor document window, the signed text is represented by
sequences of glosses. Glosses are the labels for signs. Frist, we select one technical article and
after summarizing it using previous step .Then we synthesis the words that are presented in that
summary by achieving the following steps:
1- We created a gloss for each word and then from HamNoSys window we enter the notations of
manual and non-manual movements as needed (Figure 2).

Figure 2 Forming Glosses and HamNoSys in eSign editor.
2- Saving the list of closes as a SiGML file.
3- To play any sign by avatar character we select any glosses and press the button of avatar.
which is another aspect of eSign editor is that the users can preview the signs before saving it to
do any needed modification. For example we play the sign of word internet in Arabic.
5.2.4 Database (DB) building
We used My SQL Server to build a small DB consisting of single table called words, which holds
the word IDs and signs files.
5.2.5 User Interface
We developed a simple Arabic user interface Figure 3 using PHP language that allows users to
read an Arabic article then press the summarization button to show the result of the
summarization and translation of the summary process as a set of signs in the output panel.

Figure 3: The Proposed Tool Interface
6. CONCLUSIONS
In this paper, a tool to help deaf people to get better access to the web was proposed. The
proposed tool is composed of two main phases; summarizing and translating phase. For
summarizing phase we select key terms summarizer which produce a summary. This
summarizers show promising result .Second phase include the creating of Arabic signs using 3D
avatar technology and store these signs in the database. Deaf people have difficulties in reading
skills for that reasons our tool provide a simple user interface with clear icons. Moreover,
additional improvements can be applied for this tool in the future upon the needs of deaf people.
ACKNOWLEDGEMENTS
The authors would like to thank all people who contributed in make this accomplishment.
REFERENCES
[1] Aarti Patil, Komal Pharande, Dipali Nale,and Roshani Agrawal ." Automatic text summarization."
International Journal of Computer Applications, (2015):17.
[2] Abdel-fattah, Mahmoud A. "Arabic sign language: a perspective." Journal of Deaf Studies and Deaf
Education (2005): 212-221.
[3] Ahmed Ibrahim, Tarek Elghazaly, Mervat Gheith." A Novel Arabic Text Summarization Model
Based on Rhetorical Structure Theory and Vector Space Model." International Journal of
Computational Linguistics and Natural Language Processing, (2013):480-485.
[4] Alison Wray, Stephen Cox, Mike Lincoln,and Judy Tryggvason. "A formulaic approach to
translation at the post office: reading the signs." Language & Communication, (2004):59-75.
[5] Ameera Almasoud,and Hend Al-Khalifa."SemSignWriting: A Proposed Semantic System for
Arabic Text-to-SignWriting Translation." JSEA, (2012) :604-612.
[6] Ani Nenkova. Kathleen McKeown. "Automatic summarization." Foundations and Trends® in
Information Retrieval, (2011):103-233.

[7] Aqil Azmi, and Suha Al-Thanyyan. "A text summarizer for Arabic." Computer Speech & Language,
(2012):260-273.
[8] Basmah faraj, Wafa AL-rajhi, and Yahya elhadj." Avatar Based Approach for Teaching Arabic Sign
Language." J. of Commun. &Compute. Eng, (2012) : 43.
[9] Chang, Jae-Young ,KIM, and Il-Mi. " Research Trends on Graph-Based Text Mining." International
Journal of Software Engineering and Its Applications, (2014):147-156.
[10] Daan Hermans ,Harry Knoors ,Ellen Ormel ,and Ludo Verhoeven. "The Relationship Between the
Reading and Signing Skills of Deaf Children in Bilingual Education Programs." Journal of Deaf
Studies and Deaf Education, (2008):518-530.
[11] David Inouye, Jugal K. Kalita."Comparing twitter summarization algorithms for multiple post
summaries. "In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational
Conference on Social Computing (SocialCom), (2011):298-306 .
[12] Dipanjan Das, Andr_e F.T. Martins." A survey on automatic text summarization." Literature Survey
for the Language and Statistics II course at CMU ,(2007): 192-195.
[13] Erkan, Radev ." LexPageRank: Prestige in Multi-Document Text Summarization." In EMNLP,
(2004) :365-371.
[14] Fatima AL-Khawaldeh,and Venus Samawi ."Lexical cohesion and entailment based segmentation
for arabic text summarization (lceas). " The World of Computer Science and Information Technology
Journal (WSCIT), (2015):51-60.
[15] Ganesan, K., Zhai, C., & Han, J. . "Opinosis: a graph-based approach to abstractive summarization of
highly redundant opinions." In Proceedings of the 23rd international conference on computational
linguistics,(2010):340-348.
[16] Ghaleb Al_Gaphari, Fadl M. Ba-Alwi, and Aimen Moharram ."Text Summarization using Centrality
Concept." International Journal of Computer Applications,( 2013):5-12.
[17] Ghanem, S. and Albidewi, I. (2013)." An Avatar Based Natural Arabic Sign Language Generation
System for Deaf People." Computer Engineering and Intelligent Systems, (2013):2222-1719.
[18] Hongyuan Zha."Generic summarization and keyphrase extraction using mutual reinforcement
principle and sentence clustering." In Proceedings of the 25th annual international ACM SIGIR
conference on Research and development in information retrieval ,(2002):113-120.
[19] Inderjeet Mani ."Summarization Evaluation: An Overview." In Proceedings of the NTCIR Workshop
,(2001).
[20] Judith D. Schlesinger,Dianne P. O’Leary,John M. Conroy." Arabic/English Multi-document
Summarization with CLASSY—The Past and the Future." International Conference on Intelligent
Text Processing and Computational Linguistics, (2008):568-581.
[21] Martin Hassel. "Evaluation of automatic text summarization." Licentiate Thesis, Stockholm, Sweden,
(2004):1-75.
[22] Marty Bray , David Pugalee , Claudia P. Flowers ,and Bob Algozzine. "Accessibility of Middle
Schools' Web Sites for Students with Disabilities." The Clearing House: A Journal of Educational
Strategies, Issues and Ideas, (2007):169-178.

[23] Matjaž Debevc,Primož Kosec,Andreas Holzinger." Improving multimodal web accessibility for deaf
people: sign language interpreter module." Multimedia Tools and Applications,( 2011):181-199.
[24] Mehrez Boulares, Mohamed Jemni." Toward an example-based machine translation from written text
to ASL using virtual agent animation. "arXiv preprint (2012):1203.3023.
[25] Mine Berker. "Using genetic algorithms with lexical chains for automatic text summarization. 2011.
PhD Thesis. Bogaziçi University.
[26] Mohamed Jemni ,Oussama Elghoul. "Towards Web-Based automatic interpretation of written text to
Sign Language." In First International conference on ICT & Accessibility, (2007).
[27] Mohammed Abo El-Soud , A.E. Hassan , M. S. Kandil , and Samaa M. Shohieb. "A Proposed Web
Based Framework E-Learning and Dictionary System for Deaf Arab Students. " International Journal
of Electrical & Computer Sciences IJECS-IJENS, (2010):24-50.
[28] Mohandes." Automatic translation of Arabic text to Arabic sign language." AIML Journal, (2006):
15-19.
[29] N Dheer, C Kumar."Automatic Text Summarization: A Detailed Study. "International Journal of
Science and Research . (2016): 429-433.
[30] Nabil Alami, Mohammed Meknassi, and Noureddine." Automatic texts summarization: Current state
of the art. "Journal of Asian Scientific Research,( 2015):1.
[31] Nabil Hewahi,Kathrein Abu Kwaik ."Automatic Arabic Text Summarization System (AATSS) Based
on Semantic Features Extraction. International Journal of Technology Diffusion, (2012):12-27.
[32] Peter Turney."Learning algorithms for keyphrase extraction. "Information retrieval, (2000):303-336.
[33] Qasem A. Al-Radaideh, and Mohammad Afif ."Arabic text summarization using aggregate
similarity". In the international Arab conference on information Technology. (2011).
[34] Rada Mihalcea." Graph-based ranking algorithms for sentence extraction, applied to text
summarization." In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
,(2004):20.
[35] Rafeeq Al-Hashemi . "Text Summarization Extraction System (TSES) Using Extracted Keywords. "
Int. Arab J. e-Technol .(2012): 164-168.
[36] Rasim Alguliev,and Ramiz Aliguliyev." Evolutionary algorithm for extractive text summarization."
Intelligent Information Management (2009):128-138.
[37] Rucha S. Dixit, Prof. Dr.S.S.Apte. "Improvement of Text Summarization using Fuzzy Logic Based
Method. "IOSRJCE, (2012):05-10.
[38] S.M. Halawani and Zaitun A.B. "An avatar based translation system from arabic speech to arabic sign
language for deaf people." International Journal of Information Science and Education, (2012):13-20.
[39] Shashank gupta, Anushree Jagrawal, and Neha mathur. "Automatixc Text Summarization using Page
Rank and Genetic Algorithim." Journal of Rajasthan Academy of Physical Sceiences, (2014):171-
179.

[40] Sobh, Darwish. and Fayek."Evaluation Approaches for an Arabic Extractive Generic Text
Summarization System." In: proceeding of 2nd International Conference on Arabic Language
Resource and Tools. (2009):150-155.
[41] Thomas Hanke. "HamNoSys-representing sign language data in language resources and language
processing contexts." In LREC, (2004).
[42] Waleed AlSanie, Touir, and Mathkour. "Towards an infrastructure for Arabic text summarization
using rhetorical structure theory". Master's thesis, King Saud University, Riyadh.(2005):2-70.
[43] Yihong Gong , Xin Liu." Generic text summarization using relevance measure and latent semantic
analysis." In Proceedings of the 24th annual international ACM SIGIR conference on Research and
development in information retrieval ,(2001) 19-25.
[44] Yosra Bouzid, Mohamed Jemni. "An Avatar based approach for automatically interpreting a sign
language notation." In: Advanced Learning Technologies (ICALT), 2013 IEEE 13th International
Conference on. IEEE, (2013) :92-94.
[45] Yang, H., Liao, W. and Long Lay, Y. "Text to Sign Language Conversion." ,(2001)":241-249.
AUTHORS
Ahmad A. Alhamed ,Ph.D.
Assistant Professor
Information Systems Department.
College of Computer and Information Sciences.
King Saud Universtiy, Riyadh, Saudi Arabia.
aalhamed@ksu.edu.sa
Maha Saas Alqhtani,
Teacher Assistance,
Information Systems Department.
College of Computer and Information Sciences.
King Khalid Universtiy, Abha, Saudi Arabia .

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