Clustering Top-Ranking Sentences for Information Access

  • Anastasios Tombros
  • Joemon M. Jose
  • Ian Ruthven
Conference paper

DOI: 10.1007/978-3-540-45175-4_47

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2769)
Cite this paper as:
Tombros A., Jose J.M., Ruthven I. (2003) Clustering Top-Ranking Sentences for Information Access. In: Koch T., Sølvberg I.T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2003. Lecture Notes in Computer Science, vol 2769. Springer, Berlin, Heidelberg

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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.

Personalised recommendations