Journal of Computer Science

Fuzzy Swarm Based Text Summarization

Mohammed Salem Binwahlan, Naomie Salim and Ladda Suanmali

DOI : 10.3844/jcssp.2009.338.346

Journal of Computer Science

Volume 5, Issue 5

Pages 338-346

Abstract

Problem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not.

Copyright

© 2009 Mohammed Salem Binwahlan, Naomie Salim and Ladda Suanmali. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.