MR&MR-SUM: MAXIMUM RELEVANCE AND MINIMUM REDUNDANCY DOCUMENT SUMMARIZATION MODEL

Rasim M. Alguliev (), Ramiz M. Aliguliyev () and Nijat R. Isazade ()
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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

International Journal of Information Technology & Decision Making (IJITDM), 2013, vol. 12, issue 03, 361-393

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.

Keywords: Unsupervised document summarization; maximum relevance; minimum redundancy; quadratic integer programming; differential evolution algorithm; combined similarity measure (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0219622013500156

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