Abstract
In the current paper, a system for multi-document summarization (MLDS) is developed which simultaneously optimized different quality measures to obtain a good summary. These measures include anti-redundancy, coverage, and, readability. For optimization, multi-objective binary differential evolution (MBDE) is utilized which is an evolutionary algorithm. MBDE consists of a set of solutions and each solution represents a subset of sentences to be selected in the summary. Generally, MBDE uses a single DE variant, but, here, an ensemble of two different DE variants measuring diversity among solutions and convergence towards the global optimal solution, respectively, is employed for efficient search. Three versions of the proposed model are developed varying the syntactic/semantic similarity between sentences and DE parameters selection strategy. Results are evaluated on the standard DUC datasets using ROUGE-2 measure and significant improvements of \(15.5\%\) and \(4.56\%\) are attained by the proposed approach over the two exiting techniques.
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Acknowledgement
Dr. Sriparna Saha would like to acknowledge the support of Early Career Research Award of Science and Engineering Research Board (SERB) of Department of Science and Technology India to carry out this research.
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Saini, N., Saha, S., Kumar, A., Bhattacharyya, P. (2019). Multi-document Summarization Using Adaptive Composite Differential Evolution. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_71
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