Clustering Top-Ranking Sentences for Information Access

  • Anastasios Tombros
  • Joemon M. Jose
  • Ian Ruthven
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2769)

Abstract

In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for effective information access. We present an initial evaluation of TRS clustering by comparing its effectiveness at providing access to useful information to that of document clustering.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Anastasios Tombros
    • 1
  • Joemon M. Jose
    • 1
  • Ian Ruthven
    • 2
  1. 1.Department of Computing ScienceUniversity of GlasgowGlasgowU.K.
  2. 2.Department of Computer and Information SciencesUniversity of StrathclydeGlasgowU.K.

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