Comparative Study Between Two Swarm Intelligence Automatic Text Summaries: Social Spiders vs. Social Bees

Comparative Study Between Two Swarm Intelligence Automatic Text Summaries: Social Spiders vs. Social Bees

Mohamed Amine Boudia (Dr. Tahar Moulay University of Saida, Algeria)
DOI: 10.4018/978-1-5225-3004-6.ch015

Abstract

This chapter is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization uses the bio-inspired approach to perform the results of the previous step, the objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text and minimize the sum of scores in order to increase the summarization rate. This optimization will also give a candidate's summary where the order of the phrases changes compared to the original text. For the third and final step concerning choosing a best summary from all candidate summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.
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State Of The Art

Automatic summarization appeared earlier as a field of research in computer science from the axis of NLP (automatic language processing), HP Luhn (1958) proposed in 1958 a first approach to the development of automatic abstracts from extracting phrases.

In the early 1960s, HP Edmundson and other participants in the project TRW (Thompson Ramo Wooldridge Inc.) (Edmundson et al, 1960) Proposed a new system of automatic summarization where it combined several criteria to assess the relevance of phrases to extract.

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