IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v12y2021i3p1-17.html
   My bibliography  Save this article

Particle Swarm Optimization for Punjabi Text Summarization

Author

Listed:
  • Arti Jain

    (Jaypee Institute of Information Technology, Noida, India)

  • Divakar Yadav

    (National Institute of Technology, Hamirpur, India)

  • Anuja Arora

    (Jaypee Institute of Information Technology, Noida, India)

Abstract

Particle swarm optimization (PSO) algorithm is proposed to deal with text summarization for the Punjabi language. PSO is based on intelligence that predicts among a given set of solutions which is the best solution. The search is carried out by extremely high-speed particles. It updates particle position and velocity at the end of iteration so that during the development of generations, the personal best solution and global best solution are updated. Calculation within PSO is performed using fitness function which looks into various statistical and linguistic features of the Punjabi datasets. Two Punjabi datasets—monolingual Punjabi corpus from Indian Languages Corpora Initiative Phase-II and Punjabi-Hindi parallel corpus—are considered. The parallel corpus comprises 1,000 Punjabi sentences from the tourism domain while monolingual corpus contains 30,000 Punjabi sentences of the general domain. ROUGE measures evaluate summary where the highest measure, ROUGE-1, is achieved for parallel corpus with precision, recall, and F-measure as 0.7836, 0.7957, and 0.7896, respectively.

Suggested Citation

  • Arti Jain & Divakar Yadav & Anuja Arora, 2021. "Particle Swarm Optimization for Punjabi Text Summarization," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 12(3), pages 1-17, July.
    • Handle: RePEc:igg:joris0:v:12:y:2021:i:3:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJORIS.20210701.oa1
    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:joris0:v:12:y:2021:i:3:p:1-17. 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.