Using Word Sequences for Text Summarization

  • Esaú Villatoro-Tello
  • Luis Villaseñor-Pineda
  • Manuel Montes-y-Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)


Traditional approaches for extractive summarization score/classify sentences based on features such as position in the text, word frequency and cue phrases. These features tend to produce satisfactory summaries, but have the inconvenience of being domain dependent. In this paper, we propose to tackle this problem representing the sentences by word sequences (n-grams), a widely used representation in text categorization. The experiments demonstrated that this simple representation not only diminishes the domain and language dependency but also enhances the summarization performance.


Text Categorization Word Sequence Text Summarization Relevant Sentence Bilateral Shortfall 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Esaú Villatoro-Tello
    • 1
  • Luis Villaseñor-Pineda
    • 1
  • Manuel Montes-y-Gómez
    • 1
  1. 1.Language Technologies Group, Computer Science DepartmentNational Institute of Astrophysics, Optics and Electronics (INAOE)Mexico

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