Cover Image

Fuzzy genetic semantic based text summarization

Automatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sente...

Full description

Saved in:
Bibliographic Details
Main Authors: Suanmali, Ladda, Salim, Naomie, Binwahlan, Mohammed Salem
Format: Conference or Workshop Item
Published: 2011
Online Access:http://eprints.utm.my/id/eprint/45889/
http://dx.doi.org/10.1109/DASC.2011.192
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.45889
record_format eprints
spelling my.utm.458892017-08-29T04:41:18Z http://eprints.utm.my/id/eprint/45889/ Fuzzy genetic semantic based text summarization Suanmali, Ladda Salim, Naomie Binwahlan, Mohammed Salem Automatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sentence based on a sentence scoring approach. Highly ranked sentences are used for the summary. This paper proposed an automatic text summarization approach based on sentence extraction using fuzzy logic, genetic algorithm, semantic role labeling and their combinations to generate high quality summaries. This study explored the benefits of the genetic algorithm in the optimization problem in for feature selection during the training phase and adjusts feature weights during the testing phase. Fuzzy IF-THEN rules were used to balance the weights between important and unimportant features. Conventional extraction methods cannot capture semantic relations between concepts in a text. Therefore, this research investigates the use of the semantic role labeling to capture the semantic contents in sentences and incorporate it into the summarization method. This paper is evaluated in terms of performance using ROUGE toolkit. Experimental results showed that the summaries produced by the proposed approaches are better than other approaches produced by Microsoft Word 2007, Copernic Summarizer, and MANYASPECTS summarizers. 2011 Conference or Workshop Item PeerReviewed Suanmali, Ladda and Salim, Naomie and Binwahlan, Mohammed Salem (2011) Fuzzy genetic semantic based text summarization. In: 2011 IEEE Ninth International Conference On Dependable, Autonomic And Secure Computing. http://dx.doi.org/10.1109/DASC.2011.192
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
description Automatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sentence based on a sentence scoring approach. Highly ranked sentences are used for the summary. This paper proposed an automatic text summarization approach based on sentence extraction using fuzzy logic, genetic algorithm, semantic role labeling and their combinations to generate high quality summaries. This study explored the benefits of the genetic algorithm in the optimization problem in for feature selection during the training phase and adjusts feature weights during the testing phase. Fuzzy IF-THEN rules were used to balance the weights between important and unimportant features. Conventional extraction methods cannot capture semantic relations between concepts in a text. Therefore, this research investigates the use of the semantic role labeling to capture the semantic contents in sentences and incorporate it into the summarization method. This paper is evaluated in terms of performance using ROUGE toolkit. Experimental results showed that the summaries produced by the proposed approaches are better than other approaches produced by Microsoft Word 2007, Copernic Summarizer, and MANYASPECTS summarizers.
format Conference or Workshop Item
author Suanmali, Ladda
Salim, Naomie
Binwahlan, Mohammed Salem
spellingShingle Suanmali, Ladda
Salim, Naomie
Binwahlan, Mohammed Salem
Fuzzy genetic semantic based text summarization
author_facet Suanmali, Ladda
Salim, Naomie
Binwahlan, Mohammed Salem
author_sort Suanmali, Ladda
title Fuzzy genetic semantic based text summarization
title_short Fuzzy genetic semantic based text summarization
title_full Fuzzy genetic semantic based text summarization
title_fullStr Fuzzy genetic semantic based text summarization
title_full_unstemmed Fuzzy genetic semantic based text summarization
title_sort fuzzy genetic semantic based text summarization
publishDate 2011
url http://eprints.utm.my/id/eprint/45889/
http://dx.doi.org/10.1109/DASC.2011.192
_version_ 1643651873982382080
score 13.211869

Similar Items