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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd.
2013
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/35423/1/1-s2.0-S2212017313003666-main.pdf |
| _version_ | 1825647873619918848 |
|---|---|
| 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 |
| collection | IIUM |
| 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.
|
| first_indexed | 2024-03-05T23:24:00Z |
| format | Article |
| id | oai:generic.eprints.org:35423 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T23:24:00Z |
| publishDate | 2013 |
| publisher | Elsevier Ltd. |
| record_format | dspace |
| spelling | oai:generic.eprints.org: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 |
| 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 |
| title | 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_short | CRF based feature extraction applied for supervised automatic text summarization
|
| title_sort | crf based feature extraction applied for supervised automatic text summarization |
| topic | T10.5 Communication of technical information |
| url | http://irep.iium.edu.my/35423/1/1-s2.0-S2212017313003666-main.pdf |
| work_keys_str_mv | AT kbatchanowshath crfbasedfeatureextractionappliedforsupervisedautomatictextsummarization AT aaziznormaziah crfbasedfeatureextractionappliedforsupervisedautomatictextsummarization AT ishafiesharil crfbasedfeatureextractionappliedforsupervisedautomatictextsummarization |