IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v14y2018i3p69-89.html
   My bibliography  Save this article

Multi-Documents Summarization Based on TextRank and its Application in Online Argumentation Platform

Author

Listed:
  • Caiquan Xiong

    (Hubei University of Technology, Wuhan, China)

  • Xuan Li

    (Hubei University of Technology, Wuhan, China)

  • Yuan Li

    (Hubei University of Technology, Wuhan, China)

  • Gang Liu

    (Hubei University of Technology, Wuhan, China)

Abstract

In an Online Argumentation Platform, a great deal of speech messages are produced. To find similar speech texts and extract their common summary is of great significance for improving the efficiency of argumentation and promoting consensus building. In this article, a method of speech text analysis is proposed. Firstly, a heuristic clustering algorithm is used to cluster the speech texts and obtain similar text sets. Then, an improved TextRank algorithm is used to extract a multi-document summary, and the results of the summary are fed back to experts (i.e. participants). The method of multi-document summarization is based on TextRank, which takes into account the position of sentences in paragraphs, the weight of the key sentence, and the length of the sentence. Finally, a prototype system is developed to verify the validity of the method using the four evaluation parameters of recall rate, accuracy rate, F-measure, and user feedback. The experimental results show that the method has a good performance in the system.

Suggested Citation

  • Caiquan Xiong & Xuan Li & Yuan Li & Gang Liu, 2018. "Multi-Documents Summarization Based on TextRank and its Application in Online Argumentation Platform," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 14(3), pages 69-89, July.
    • Handle: RePEc:igg:jdwm00:v:14:y:2018:i:3:p:69-89
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2018070104
    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:jdwm00:v:14:y:2018:i:3:p:69-89. 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.