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Mexican International Conference on Artificial Intelligence

MICAI 2008: MICAI 2008: Advances in Artificial Intelligence pp 123–132Cite as

Effect of Preprocessing on Extractive Summarization with Maximal Frequent Sequences

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5317))

Abstract

The task of extractive summarization consists in producing a text summary by extracting a subset of text segments, such as sentences, and concatenating them to form a summary of the original text. The selection of sentences is based on terms they contain, which can be single words or multiword expressions. In a previous work, we have suggested so-called Maximal Frequent Sequences as such terms. In this paper, we investigate the effect of preprocessing on the process of selecting such sequences. Our results suggest that the accuracy of the method is, contrary to expectations, not seriously affected by preprocessing—which is both bad and good news, as we show.

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Author information

Authors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Av. Juan de Dios Bátiz s/n, D.F., 07738, Mexico

    Yulia Ledeneva

Authors
  1. Yulia Ledeneva
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro #1 , Sta. María Tonantzintla, 72840, Puebla, México

    Eduardo F. Morales

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Ledeneva, Y. (2008). Effect of Preprocessing on Extractive Summarization with Maximal Frequent Sequences. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_11

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  • DOI: https://doi.org/10.1007/978-3-540-88636-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88635-8

  • Online ISBN: 978-3-540-88636-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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