Hybrid differential evolution based automatic single document text summarization

Automatic single document text summarization is a process of condensing an input text document. In this process, a summary extraction approach summarizes a document by extracting the most informative sentences in a document. To select such sentences, a sentence scoring approach is used to assign a s...

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Main Author:	Mohammed Ali Abuobieda, Albaraa Abuobieda
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/38967/5/AlbaraaAbuobiedaPFSKSM2013.pdf
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id	my-utm-ep.38967
record_format	uketd_dc
spelling	my-utm-ep.389672017-06-22T02:47:20Z Hybrid differential evolution based automatic single document text summarization 2013-09 Mohammed Ali Abuobieda, Albaraa Abuobieda QA75 Electronic computers. Computer science Automatic single document text summarization is a process of condensing an input text document. In this process, a summary extraction approach summarizes a document by extracting the most informative sentences in a document. To select such sentences, a sentence scoring approach is used to assign a score for each input sentence before ranking them accordingly. Based on user defined summary ratio, only top ranked sentences are selected to be part of the summary and selecting the most informative sentences is a challenge for extractive based automatic text summarization researchers. Thus, this research proposed extraction based automatic single document text summarization methods by investigating a single meta-heuristic evolutionary algorithm called Differential Evolution (DE) to generate high quality summaries. The DE algorithm is used (i) to find out the best feature weight score to discriminate between important and non-important features, (ii) to perform as a cluster machine learning method using Normalized Google Distance and Jaccard similarity measures to generate a highly diversed summary, (iii) to employ opposition-based learning (OBL) approach to improve the performance of the DE algorithm and (iv) to develop a hybrid model used to investigate the adavantages of the combination of feature weighting, diversity and OBL approaches. To evaluate the proposed methods, the standard dataset from Document Understanding Conference (DUC) 2002 and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as the standard evaluation measurement toolkit were used. Experimental results showed that the hybrid models as well as all the proposed individual methods performed well for text summarization as compared to four benchmark methods: Microsoft Word, Copernic, the best DUC 2002, the worst DUC 2002 summarizers and a human against another human summarizer. In addition, the proposed methods in the DE algorithm outperformed Genetic Algorithm and fuzzy swarm diversity based methods evolutionary based algorithms. The results of the experiments have proven that the proposed hybrid models generate better quality text-summaries. 2013-09 Thesis http://eprints.utm.my/id/eprint/38967/ http://eprints.utm.my/id/eprint/38967/5/AlbaraaAbuobiedaPFSKSM2013.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohammed Ali Abuobieda, Albaraa Abuobieda Hybrid differential evolution based automatic single document text summarization
description	Automatic single document text summarization is a process of condensing an input text document. In this process, a summary extraction approach summarizes a document by extracting the most informative sentences in a document. To select such sentences, a sentence scoring approach is used to assign a score for each input sentence before ranking them accordingly. Based on user defined summary ratio, only top ranked sentences are selected to be part of the summary and selecting the most informative sentences is a challenge for extractive based automatic text summarization researchers. Thus, this research proposed extraction based automatic single document text summarization methods by investigating a single meta-heuristic evolutionary algorithm called Differential Evolution (DE) to generate high quality summaries. The DE algorithm is used (i) to find out the best feature weight score to discriminate between important and non-important features, (ii) to perform as a cluster machine learning method using Normalized Google Distance and Jaccard similarity measures to generate a highly diversed summary, (iii) to employ opposition-based learning (OBL) approach to improve the performance of the DE algorithm and (iv) to develop a hybrid model used to investigate the adavantages of the combination of feature weighting, diversity and OBL approaches. To evaluate the proposed methods, the standard dataset from Document Understanding Conference (DUC) 2002 and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as the standard evaluation measurement toolkit were used. Experimental results showed that the hybrid models as well as all the proposed individual methods performed well for text summarization as compared to four benchmark methods: Microsoft Word, Copernic, the best DUC 2002, the worst DUC 2002 summarizers and a human against another human summarizer. In addition, the proposed methods in the DE algorithm outperformed Genetic Algorithm and fuzzy swarm diversity based methods evolutionary based algorithms. The results of the experiments have proven that the proposed hybrid models generate better quality text-summaries.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammed Ali Abuobieda, Albaraa Abuobieda
author_facet	Mohammed Ali Abuobieda, Albaraa Abuobieda
author_sort	Mohammed Ali Abuobieda, Albaraa Abuobieda
title	Hybrid differential evolution based automatic single document text summarization
title_short	Hybrid differential evolution based automatic single document text summarization
title_full	Hybrid differential evolution based automatic single document text summarization
title_fullStr	Hybrid differential evolution based automatic single document text summarization
title_full_unstemmed	Hybrid differential evolution based automatic single document text summarization
title_sort	hybrid differential evolution based automatic single document text summarization
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/38967/5/AlbaraaAbuobiedaPFSKSM2013.pdf
_version_	1747816534834675712

Hybrid differential evolution based automatic single document text summarization

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