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Questionnaire Free Text Summarisation Using Hierarchical Classification

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Research and Development in Intelligent Systems XXIX (SGAI 2012)

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Abstract

This paper presents an investigation into the summarisation of the free text element of questionnaire data using hierarchical text classification. The process makes the assumption that text summarisation can be achieved using a classification approach whereby several class labels can be associated with documents which then constitute the summarisation. A hierarchical classification approach is suggested which offers the advantage that different levels of classification can be used and the summarisation customised according to which branch of the tree the current document is located. The approach is evaluated using free text from questionnaires used in the SAVSNET (Small Animal Veterinary Surveillance Network) project. The results demonstrate the viability of using hierarchical classification to generate free text summaries.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool, Liverpool, L69 3BX, UK

    Matias Garcia-Constantino & Frans Coenen

  2. School of Veterinary Science, University of Liverpool, Leahurst, Neston, CH64 7TE, UK

    P-J Noble & Alan Radford

Authors
  1. Matias Garcia-Constantino
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  2. Frans Coenen
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  3. P-J Noble
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  4. Alan Radford
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Corresponding author

Correspondence to Matias Garcia-Constantino .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Whitepost Lane The Lilacs, Portsmouth, PO1 3AH, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering & Mathe, University of Brighton, Lewes Road, Brighton, BN2 4GJ, West Sussex, United Kingdom

    Miltos Petridis

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© 2012 Springer-Verlag London

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Garcia-Constantino, M., Coenen, F., Noble, PJ., Radford, A. (2012). Questionnaire Free Text Summarisation Using Hierarchical Classification. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_3

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  • DOI: https://doi.org/10.1007/978-1-4471-4739-8_3

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4738-1

  • Online ISBN: 978-1-4471-4739-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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