Research and Advanced Technology for Digital Libraries

Volume 2769 of the series Lecture Notes in Computer Science pp 523-528

Clustering Top-Ranking Sentences for Information Access

  • Anastasios TombrosAffiliated withDepartment of Computing Science, University of Glasgow
  • , Joemon M. JoseAffiliated withDepartment of Computing Science, University of Glasgow
  • , Ian RuthvenAffiliated withDepartment of Computer and Information Sciences, University of Strathclyde

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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.