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Filtering for Profile-Biased Multi-document Summarization

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Advances in Information Retrieval (ECIR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3408))

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Abstract

In this article, we present an information filtering method that selects from a set of documents their most significant excerpts in relation to a user profile. This method relies on both structured profiles and a topical analysis of documents. The topical analysis is also used for expanding a profile in relation to a particular document by selecting the terms of the document that are closely linked to those of the profile. This expansion is a way for selecting in a more reliable way excerpts that are linked to profiles but also for selecting excerpts that may bring new and interesting information about their topics. This method was implemented by the REDUIT system, which was successfully evaluated for document filtering and passage extraction.

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Author information

Authors and Affiliations

  1. CEA-LIST/LIC2M, 18, route du Panorama, BP6, 92265, Fontenay-aux-Roses Cedex

    Sana Leila Châar, Olivier Ferret & Christian Fluhr

Authors
  1. Sana Leila Châar
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  2. Olivier Ferret
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  3. Christian Fluhr
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Editor information

Editors and Affiliations

  1. Departamento de Electrónica y Computación, Universidad de Santiago de Compostela, Spain

    David E. Losada

  2. Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, Universidad de Granada, 18071, Granada, Spain

    Juan M. Fernández-Luna

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© 2005 Springer-Verlag Berlin Heidelberg

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Châar, S.L., Ferret, O., Fluhr, C. (2005). Filtering for Profile-Biased Multi-document Summarization. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_10

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  • DOI: https://doi.org/10.1007/978-3-540-31865-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25295-5

  • Online ISBN: 978-3-540-31865-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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