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MCMR: Maximum coverage and minimum redundant text summarization model
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DOI：10.1016/j.eswa.2011.05.033.png)
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摘要
En 中文
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. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Text summarization
Maximum coverage
Less redundancy
Integer linear programming
Particle swarm optimization
Branch-and-bound
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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