• DocumentCode
    2616663
  • Title

    Intelligent Extractive Text Summarization Using Fuzzy Inference Systems

  • Author

    Kiani-B, Arman ; Akbarzadeh-T, M.-R. ; Moeinzadeh, M.H.

  • Author_Institution
    Dept. of Electr. Eng., Mashhad Ferdowsi Univ.
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    With the growing size of textual information on the World Wide Web, automatic data summarization becomes essential for both Web users as well as data base and search engine developers. In this paper, we propose a novel approach for extracting the most relevant sentences from an original document to form a summary. The approach utilizes fuzzy measures and inference to find the most significant sentences. Experimental results reveal that the proposed approach extracts the more relevant sentences when compared with two commercially available text summarizers
  • Keywords
    abstracting; fuzzy reasoning; text analysis; fuzzy inference systems; intelligent extractive text summarization; membership functions; parsing; Data engineering; Data mining; Decision trees; Engines; Fuzzy systems; Humans; ISO standards; Natural language processing; Probability; Statistical learning; Fuzzy Inference Systems; Membership Functions; Parsing; Text Summarization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering of Intelligent Systems, 2006 IEEE International Conference on
  • Conference_Location
    Islamabad
  • Print_ISBN
    1-4244-0456-8
  • Type

    conf

  • DOI
    10.1109/ICEIS.2006.1703156
  • Filename
    1703156