Multi-document text summarization using text clustering for Arabic Language

The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text...

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Main Author:	Waheeb, Samer Abdulateef
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/4373/1/s812273.pdf https://etd.uum.edu.my/4373/7/s812273_abstract.pdf
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id	my-uum-etd.4373
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Husni, Husniza Ahmad, Faudziah
topic	QA76 Computer software
spellingShingle	QA76 Computer software Waheeb, Samer Abdulateef Multi-document text summarization using text clustering for Arabic Language
description	The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text summarization is redundancy of sentences, and thus, redundancy must be eliminated to ensure coherence, and improve readability. Hence, we set out the main objective as to examine multi-document summarization salient information for text Arabic summarization task with noisy and redundancy information. In this research we used Essex Arabic Summaries Corpus (EASC) as data to test and achieve our main objective and of course its subsequent subobjectives. We used the token process to split the original text into words, and then removed all the stop words, and then we extract the root of each word, and then represented the text as bag of words by TFIDF without the noisy information. In the second step we applied the K-means algorithm with cosine similarity in our experimental to select the best cluster based on cluster ordering by distance performance. We applied SVM to order the sentences after selected the best cluster, then we selected the highest weight sentences for the final summary to reduce redundancy information. Finally, the final summary results for the ten categories of related documents are evaluated using Recall and Precision with the best Recall achieved is 0.6 and Precision is 0.6.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Waheeb, Samer Abdulateef
author_facet	Waheeb, Samer Abdulateef
author_sort	Waheeb, Samer Abdulateef
title	Multi-document text summarization using text clustering for Arabic Language
title_short	Multi-document text summarization using text clustering for Arabic Language
title_full	Multi-document text summarization using text clustering for Arabic Language
title_fullStr	Multi-document text summarization using text clustering for Arabic Language
title_full_unstemmed	Multi-document text summarization using text clustering for Arabic Language
title_sort	multi-document text summarization using text clustering for arabic language
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2014
url	https://etd.uum.edu.my/4373/1/s812273.pdf https://etd.uum.edu.my/4373/7/s812273_abstract.pdf
_version_	1776103639911235584
spelling	my-uum-etd.43732023-01-17T07:54:34Z Multi-document text summarization using text clustering for Arabic Language 2014 Waheeb, Samer Abdulateef Husni, Husniza Ahmad, Faudziah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA76 Computer software The process of multi-document summarization is producing a single summary of a collection of related documents. In this work we focus on generic extractive Arabic multi-document summarizers. We also describe the cluster approach for multi-document summarization. The problem with multi-document text summarization is redundancy of sentences, and thus, redundancy must be eliminated to ensure coherence, and improve readability. Hence, we set out the main objective as to examine multi-document summarization salient information for text Arabic summarization task with noisy and redundancy information. In this research we used Essex Arabic Summaries Corpus (EASC) as data to test and achieve our main objective and of course its subsequent subobjectives. We used the token process to split the original text into words, and then removed all the stop words, and then we extract the root of each word, and then represented the text as bag of words by TFIDF without the noisy information. In the second step we applied the K-means algorithm with cosine similarity in our experimental to select the best cluster based on cluster ordering by distance performance. We applied SVM to order the sentences after selected the best cluster, then we selected the highest weight sentences for the final summary to reduce redundancy information. Finally, the final summary results for the ten categories of related documents are evaluated using Recall and Precision with the best Recall achieved is 0.6 and Precision is 0.6. 2014 Thesis https://etd.uum.edu.my/4373/ https://etd.uum.edu.my/4373/1/s812273.pdf text eng public https://etd.uum.edu.my/4373/7/s812273_abstract.pdf text eng public masters masters Universiti Utara Malaysia Abbas, M., Smaili, K., & Berkani, D. (2009a). Comparing TR-Classifier and KNN by using Reduced Sizes of Vocabularies. Culture, 1, 210. Abbas, M., Smaili, K., & Berkani, D. (2009b). A trigger-based classifier. Paper presented at the The 2nd Int. Conf. on Arabic Language Resources and Tools (MEDAR 2009). Agarwal, N., Reddy, R. S., Gvr, K., & Rosé, C. P. (2011). Towards multi-document summarization of scientific articles: making interesting comparisons with SciSumm. ACL HLT 2011, 8. Al-Sulaiti, L., & Atwell, E. (2004). Designing and developing a corpus of contemporary Arabic. 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Multi-document text summarization using text clustering for Arabic Language

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