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Automatic generic document summarization based on non-negative matrix factorization
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DOI：10.1016/j.ipm.2008.06.002.png)
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In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Generic summarization
NMF
LSA
Semantic feature
Semantic variable
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