Comparative Study of DE and PSO over Document Summarization

With the exponential growth in the quantity and complexity of information sources, a number of computational intelligent-based techniques have developed in literature for document summarization. In this paper, a comparative study of two population-based stochastic optimization techniques has been proposed for document summarization. It specifies the relationship among sentences based on similarity and minimizes the weight of each sentence to extract summary sentences at different compression level. Comparison of both the optimization techniques based on fallout value of extracted sentences shows the good performance of PSO compared to DE on five different English corpus data.

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