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.
Highlights
The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources
We found that the overlapping between the two human summaries (H2 and H1) which we used in this study is 49% similar to each other
The purpose of using the human summarizer (H2-H1) as benchmark is to show how much the performance of the proposed method, the swarm model and MS word summarizer (Msword) summarizers is acceptable compared with that performance of the human (H2-H1)
Summary
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 achieving of such aim as a big challenge. In this study, we investigate the incorporation of fuzzy logic with swarm intelligence. In automatic text summarization where the sentence score is based on the weights of the features, choosing those weights can be imprecise and uncertain, by the incorporation of fuzzy logic with swarm intelligence, so that risks, uncertainty, ambiguity and imprecise values can be flexibility tolerated. Automatic text summarization researchers since Luhn’s research[1], they are trying to solve or at least relieve the challenge by proposing techniques for generating summaries. The summaries serve as quick guide to the interesting information, providing a short form for each document in the document set; reading summary makes decision about reading the whole document or not and it serves as time saver
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