DESAMC+DocSum: Differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization

Multi-document summarization is used to extract the main ideas of the documents and put them into a short summary. In 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 salient sentences from given documents while reducing redundant information in the summaries and maximizing the summary relevancy. The model is represented as a modified p-median problem. The proposed approach not only expresses sentence-to-sentence relationship, but also expresses summary-to-document and summary-to-subtopics relationships. To solve the optimization problem a new differential evolution algorithm based on self-adaptive mutation and crossover parameters, called DESAMC, is proposed. Experimental studies on DUC benchmark data show the good performance of proposed model and its potential in summarization tasks. (C) 2012 Elsevier B.V. All rights reserved.