Document (#34449)

Author

Lee, J.-H.
Park, S.
Ahn, C.-M.
Kim, D.

Title

Automatic generic document summarization based on non-negative matrix factorization

Source

Information processing and management. 45(2009) no.1, S.20-34

Year

2009

Abstract

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.

Theme

Automatisches Abstracting

Object

Latent semantic analysis

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5. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.20

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