Abstract
Automatic text summarization is leading topic of information retrieval research due to increasing online transfer of information. The large volume of information is limited due to constraint of memory devices and access time. The existing summarization system uses the sentence extraction technique where the important sentences are extracted and presented as summary. Various summarization methods are used which do not take context into consideration. The proposed system focuses on multi-document summarization which is based on context score. Bernoulli model of randomness is used to provide an informative score of bi-gram terms based on lexical association. The resulting weight is then used in the graph-based iterative algorithm to generate a summary. Experiments have been conducted over the self-generated 100 document and benchmark DUC data sets. It has been shown that proposed system outperforms the existing methods.
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Sonawane, S., Ghotkar, A., Hinge, S. (2019). Context-Based Multi-document Summarization. In: Mandal, J., Sinha, D., Bandopadhyay, J. (eds) Contemporary Advances in Innovative and Applicable Information Technology. Advances in Intelligent Systems and Computing, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-13-1540-4_16
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DOI: https://doi.org/10.1007/978-981-13-1540-4_16
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