Text summarization using a trainable summarizer and latent semantic analysis

  • Authors:

  • Jen-Yuan Yeh;Hao-Ren Ke;Wei-Pang Yang;I-Heng Meng

  • Affiliations:

  • Department of Computer & Information Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC;Digital Library & Information Section of Library, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC;Department of Computer & Information Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC;Department of Computer & Information Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 30050, Taiwan, ROC

  • Venue:
  • Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
  • Year:
  • 2005

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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.