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Sentence selection for generic document summarization using an adaptive differential evolution algorithm
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AI
DOI：10.1016/j.swevo.2011.06.006.png)
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Abstract
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For effective multi-document summarization, it is important to reduce redundant information in the summaries and extract sentences, which are common to given documents. This paper presents a document summarization model which extracts key sentences from given documents while reducing redundant information in the summaries. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The model is represented as a discrete optimization problem. To solve the discrete optimization problem in this study an adaptive DE algorithm is created. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is to be preferred over summarization systems. We also showed that the resulting summarization system based on the proposed optimization approach is competitive on the DUC2002 and DUC2004 datasets. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Document summarization
Redundancy
Content coverage
Optimization model
Adaptive differential evolution
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