Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking\ninto account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy\nsummaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has\nbeen proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward\nlocal optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming\ntask. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability.\nThis paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical\ndifferential evolution. We implemented our model on multi-document summarization task. Experiments have shown that\nthe proposed model is competitive on the DUC2006 dataset.
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