Abstract
Multi document summarization is the process of automatic creation of a summary of one or more text documents. We developed a multi-document summarization system which generate an extractive generic summary with maximum relevance and minimum redundancy. To achieve this, four features associated with sentences, that can influence the summarization process are extracted. It is difficult to find the appropriate weights corresponding to the features, which leads to good results. We propose a metaheuristic optimization based on solution population with multiple objective functions. The objective functions used takes care of both the statistical and semantic aspects of the documents. Our population based optimization converges rapidly to produce candidate sentences for summary. Evaluation of the proposed system is performed on DUC 2002 dataset using ROGUE tool kit. Experimental results shows that our system outperforms the state of the art works in terms of Recall and Precision.
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Premjith, P.S., John, A., Wilscy, M. (2015). Metaheuristic Optimization Using Sentence Level Semantics for Extractive Document Summarization. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_33
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DOI: https://doi.org/10.1007/978-3-319-26832-3_33
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