Document (#33004)

Author

Yeh, J.-Y.
Ke, H.-R.
Yang, W.-P.
Meng, I.-H.

Title

Text summarization using a trainable summarizer and latent semantic analysis

Source

Information processing and management. 41(2005) no.1, S.75-95

Year

2005

Abstract

This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.

Theme

Automatisches Abstracting

Object

Latent Semantic Indexing

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5. Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009) 0.27

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