MR&MR-SUM: MAXIMUM RELEVANCE AND MINIMUM REDUNDANCY DOCUMENT SUMMARIZATION MODEL
Rasim M. Alguliev (),
Ramiz M. Aliguliyev () and
Nijat R. Isazade ()
Additional contact information
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
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622013500156
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:12:y:2013:i:03:n:s0219622013500156
Ordering information: This journal article can be ordered from
DOI: 10.1142/S0219622013500156
Access Statistics for this article
International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi
More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().