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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/35423/1/1-s2.0-S2212017313003666-main.pdf
http://irep.iium.edu.my/35423/
http://www.sciencedirect.com
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spelling my.iium.irep.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 REM 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
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/35423/1/1-s2.0-S2212017313003666-main.pdf
http://irep.iium.edu.my/35423/
http://www.sciencedirect.com
_version_ 1643610788134387712
score 13.211869

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