Text summarization using a trainable summarizer and latent semantic analysis

Jen Yuan Yeh*, Hao Ren Ke, Wei Pang Yang, I. Heng Meng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

213 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)75-95
Number of pages21
JournalInformation Processing and Management
Issue number1
Publication statusPublished - 2005 Jan
Externally publishedYes


  • Corpus-based approach
  • Latent semantic analysis
  • Text relationship map
  • Text summarization

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences


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