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 (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Algeria), Reda Mohamed Hamou (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Algeria) and Abdelmalek Amine (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Algeria)
Copyright: © 2018 |Pages: 25
DOI: 10.4018/IJAMC.2018010102

Abstract

This article 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 use the bio-inspired approach to performs the results of the previous step. Its 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, minimize the sum of scores in order to increase the summarization rate; this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final step concerned in choosing a best summary from all candidates summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.
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2. State Of Art

Automatic summarization appeared earlier as a field of research in computer science from the axis of NLP (automatic language processing), HP Luhn (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, 1960) Proposed a new system of automatic summarization where it combined several criteria to assess the relevance of phrases to extract.

These works were made to identify the fundamental ideas around the automatic summarization, such as problems caused by extraction to build summaries (problems of redundancy, incompleteness, break, etc.), the theoretical inadequacy of the use of statistics, or the difficulties to understand a text (from semantic analysis) to summarize.

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