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Mr&Mr-Sum: Maximum Relevance And Minimum Redundancy Document Summarization Model

Author

Listed:
  • RASIM M. ALGULIEV

    (Institute of Information Technology of Azerbaijan National Academy of Sciences, 9, B. Vahabzade Street, Baku AZ1141, Azerbaijan)

  • RAMIZ M. ALIGULIYEV

    (Institute of Information Technology of Azerbaijan National Academy of Sciences, 9, B. Vahabzade Street, Baku AZ1141, Azerbaijan)

  • NIJAT R. ISAZADE

    (Institute of Information Technology of Azerbaijan National Academy of Sciences, 9, B. Vahabzade Street, Baku AZ1141, Azerbaijan)

Abstract

We have presented an approach to automatic document summarization. In the proposed approach, text summarization is modeled as a quadratic integer-programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. To solve the optimization problem we have created a novel differential evolution algorithm. Experimental results on DUC2005 and DUC2007 data sets showed that the proposed approach outperforms the other methods.

Suggested Citation

  • Rasim M. Alguliev & Ramiz M. Aliguliyev & Nijat R. Isazade, 2013. "Mr&Mr-Sum: Maximum Relevance And Minimum Redundancy Document Summarization Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 361-393.
    • Handle: RePEc:wsi:ijitdm:v:12:y:2013:i:03:n:s0219622013500156
    DOI: 10.1142/S0219622013500156
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