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

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

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RAMIZ M. ALIGULIYEV

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

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, and

NIJAT R. ISAZADE

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

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https://doi.org/10.1142/S0219622013500156Cited by:3 (Source: Crossref)

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.

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