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To solve the optimization problem a self\u2010adaptive 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\u2010adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self\u2010adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi\u2010document summarization task. 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