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Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

We present an approach to the design of an automatic text summarizer that generates a summary by extracting sentence segments. First, sentences are broken into segments by special cue markers. Each segment is represented by a set of predefined features (e.g. location of the segment, number of title words in the segment). Then supervised learning algorithms are used to train the summarizer to extract important sentence segments, based on the feature vector. Results of experiments indicate that the performance of the proposed approach compares quite favorably with other approaches (including MS Word summarizer).

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References

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, UCLA, Los Angeles, CA, 90095, USA

    Wesley T. Chuang

  2. HRL Laboratories, LLC, 3011 Malibu Canyon Rd, Malibu, CA, 90265, USA

    Wesley T. Chuang & Jihoon Yang

Authors
  1. Wesley T. Chuang
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  2. Jihoon Yang
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Chuang, W.T., Yang, J. (2000). Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_52

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  • DOI: https://doi.org/10.1007/3-540-45571-X_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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