Text Summarization Method Based on Normalized Non-Negative Matrix Factorizationстатья

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[1] Tsarev D. V., Mashechkev I. V., Petrovskiy M. I. Text summarization method based on normalized non-negative matrix factorization // Proceedings of the 3rd International Conference on Mechanical and Electrical Technology (ICMET 2011). — Vol. 1. — ASME (www.asme.org) 3 Park Avenue, New York, NY 10016, USA, 2011. — P. 563–567. This paper presents a new generic text summarization method using Nonnegative Matrix Factorization (NMF) to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of NMF topic space (or feature space) and further weighting of each topic using sentences representation in topic space. Required number of sentences with the highest relevance values is selected for the summary. The number of sentences is defined by the length of the demanded summary. The developed method has been experimentally verified on DUC 2002 standard dataset and it has shown the better summarization quality and performance than state of the art methods.

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