MCMR: Maximum coverage and minimum redundant text summarization model

Authors:
Rasim M. Alguliev;Ramiz M. Aliguliyev;Makrufa S. Hajirahimova;Chingiz A. Mehdiyev
Affiliations:
Institute of Information Technology of National Academy of Sciences of Azerbaijan, Azerbaijan;Institute of Information Technology of National Academy of Sciences of Azerbaijan, Azerbaijan;Institute of Information Technology of National Academy of Sciences of Azerbaijan, Azerbaijan;Institute of Information Technology of National Academy of Sciences of Azerbaijan, Azerbaijan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 39
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Abstract

In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover key sentences in the given document(s) and cover the main content of the original document(s). This model also guarantees that in the summary can not be multiple sentences that convey the same information. The proposed model is quite general and can also be used for single- and multi-document summarization. We implemented our model on multi-document summarization task. Experimental results on DUC2005 and DUC2007 datasets showed that our proposed approach outperforms the baseline systems.