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

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  • 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)

  • Abdelmalek Amine

    (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saïda, Algeria)

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.

Suggested Citation

  • Mohamed Amine Boudia & Reda Mohamed Hamou & Abdelmalek Amine, 2018. "Comparative Study Between Two Swarm Intelligence Automatic Text Summaries: Social Spiders vs Social Bees," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(1), pages 15-39, January.
    • Handle: RePEc:igg:jamc00:v:9:y:2018:i:1:p:15-39
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    Cited by:

    1. Khin Sandar Kyaw & Somchai Limsiroratana & Tharnpas Sattayaraksa, 2022. "A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-34, January.

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