Abstract
Extractive multidocument summarization is modeled as a modified p‐median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self‐adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self‐adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self‐adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi‐document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.
Highlights
Automatic document summarization has drawn increasing attention in the past with rapid growth of the Internet and electronic government
Multidocument summarization can be considered as an extension of single-document summarization and used for precisely describing the information contained in a cluster of documents and facilitate users to understand the document cluster
Methods pSum-self-adaptive DE (SaDE) LEX TMR + TF HybHSum PLSA-JS interactive ranking (iRANK) HierSum Support Vector Regression (SVR)
Summary
Automatic document summarization has drawn increasing attention in the past with rapid growth of the Internet and electronic government. The explosion of electronic documents has led to information overload, implying that finding and using information efficiently and effectively has become a pressingly practical problem. The information overload can be reduced by text summarization together with conventional search engines to efficiently access the relevance of retrieved documents. Multidocument summarization can be considered as an extension of single-document summarization and used for precisely describing the information contained in a cluster of documents and facilitate users to understand the document cluster. Since it combines and integrates the information across documents, it performs knowledge synthesis and knowledge discovery and can be used for knowledge acquisition [2]
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