CRF based feature extraction applied for supervised automatic text summarization

Feature extraction is the promising issue to be addressed in algebraic based Automatic Text Summarization (ATS) methods. The most vital role of any ATS is the identification of most important sentences from the given text. This is possible only when the correct features of the sentences are iden...

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Main Authors:	K. Batcha, Nowshath, A. Aziz, Normaziah, I. Shafie, Sharil
Format:	Article
Language:	English
Published:	Elsevier Ltd. 2013
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/35423/ http://irep.iium.edu.my/35423/ http://irep.iium.edu.my/35423/1/1-s2.0-S2212017313003666-main.pdf

id	iium-35423
recordtype	eprints
spelling	iium-354232014-09-15T02:41:45Z http://irep.iium.edu.my/35423/ CRF based feature extraction applied for supervised automatic text summarization K. Batcha, Nowshath A. Aziz, Normaziah I. Shafie, Sharil T10.5 Communication of technical information Feature extraction is the promising issue to be addressed in algebraic based Automatic Text Summarization (ATS) methods. The most vital role of any ATS is the identification of most important sentences from the given text. This is possible only when the correct features of the sentences are identified properly. Hence this paper proposes a Conditional Random Field (CRF) based ATS which can identify and extract the correct features which is the main issue that exists with the Non-negative Matrix Factorization (NMF) based ATS. This work proposes a trainable supervised method. Result clearly indicates that the newly proposed approach can identify and segment the sentences based on features more accurately than the existing method addressed. Elsevier Ltd. 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/35423/1/1-s2.0-S2212017313003666-main.pdf K. Batcha, Nowshath and A. Aziz, Normaziah and I. Shafie, Sharil (2013) CRF based feature extraction applied for supervised automatic text summarization. Procedia Technology , 11. pp. 426-436. ISSN 2212-0173 http://www.sciencedirect.com
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information K. Batcha, Nowshath A. Aziz, Normaziah I. Shafie, Sharil CRF based feature extraction applied for supervised automatic text summarization
description	Feature extraction is the promising issue to be addressed in algebraic based Automatic Text Summarization (ATS) methods. The most vital role of any ATS is the identification of most important sentences from the given text. This is possible only when the correct features of the sentences are identified properly. Hence this paper proposes a Conditional Random Field (CRF) based ATS which can identify and extract the correct features which is the main issue that exists with the Non-negative Matrix Factorization (NMF) based ATS. This work proposes a trainable supervised method. Result clearly indicates that the newly proposed approach can identify and segment the sentences based on features more accurately than the existing method addressed.
format	Article
author	K. Batcha, Nowshath A. Aziz, Normaziah I. Shafie, Sharil
author_facet	K. Batcha, Nowshath A. Aziz, Normaziah I. Shafie, Sharil
author_sort	K. Batcha, Nowshath
title	CRF based feature extraction applied for supervised automatic text summarization
title_short	CRF based feature extraction applied for supervised automatic text summarization
title_full	CRF based feature extraction applied for supervised automatic text summarization
title_fullStr	CRF based feature extraction applied for supervised automatic text summarization
title_full_unstemmed	CRF based feature extraction applied for supervised automatic text summarization
title_sort	crf based feature extraction applied for supervised automatic text summarization
publisher	Elsevier Ltd.
publishDate	2013
url	http://irep.iium.edu.my/35423/ http://irep.iium.edu.my/35423/ http://irep.iium.edu.my/35423/1/1-s2.0-S2212017313003666-main.pdf
first_indexed	2023-09-18T20:50:47Z
last_indexed	2023-09-18T20:50:47Z
_version_	1777409980137209856

CRF based feature extraction applied for supervised automatic text summarization

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