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CDDS: Constraint-driven document summarization models
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DOI：10.1016/j.eswa.2012.07.049.png)
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Abstract
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This paper proposes a constraint-driven document summarization approach emphasizing the following two requirements: (1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary and (2) sufficient coverage, which focuses on avoiding the loss of the document's main information when generating the summary. The constraint-driven document summarization models with tuning the constraint parameters can drive content coverage and diversity in a summary. The models are formulated as a quadratic integer programming (QIP) problem. To solve the QIP problem we used a discrete PSO algorithm. The models are implemented on multi-document summarization task. The comparative results showed that the proposed models outperform other methods on DUC2005 and DUC2007 datasets. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Constraint-driven summarization
Coverage-driven summarization
Diversity-driven summarization
Quadratic integer programming
Particle swarm optimization
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