IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v9y2015i3p65-86.html
   My bibliography  Save this article

Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries

Author

Listed:
  • Mohamed Amine Boudia

    (Department of Computer Science, Laboratory Knowledge Management and Complex Data (GeCoDe Lab), Dr. Moulay Tahar University Saïda, Saïda, Algeria)

  • Reda Mohamed Hamou

    (Department of Computer Science, Laboratory Knowledge Management and Complex Data (GeCoDe Lab), Dr. Moulay Tahar University Saïda, Saïda, Algeria)

  • Abdelmalek Amine

    (Department of Computer Science, Laboratory Knowledge Management and Complex Data (GeCoDe Lab), Dr. Moulay Tahar University Saïda, Saïda, Algeria)

  • Mohamed Elhadi Rahmani

    (Department of Computer Science, Laboratory Knowledge Management and Complex Data (GeCoDe Lab), Dr. Moulay Tahar University Saïda, Saïda, Algeria)

  • Amine Rahmani

    (Department of Computer Science, Laboratory Knowledge Management and Complex Data (GeCoDe Lab), Dr. Moulay Tahar University Saïda, Saïda, Algeria)

Abstract

The authors propose a new multilayer approach for automatic text summaries. In the first layer, they 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 second layer aims to optimize the results of the previous layer by a meta-heuristic based on social spiders. 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 layer concerned in choosing a best summary from all candidates summaries generated by optimization layer, we opted for the technique of voting with a simple majority.

Suggested Citation

  • Mohamed Amine Boudia & Reda Mohamed Hamou & Abdelmalek Amine & Mohamed Elhadi Rahmani & Amine Rahmani, 2015. "Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 9(3), pages 65-86, July.
  • Handle: RePEc:igg:jcini0:v:9:y:2015:i:3:p:65-86
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2015070104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:9:y:2015:i:3:p:65-86. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.