Clustering top-ranking sentences for information access

A. Tombros, J. Jose, I. Ruthven, T. Koch (Editor), I.T. Solvberg (Editor)

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationResearch and AdvancedTechnology for Digital Libraries
Place of PublicationBerlin
Number of pages5
Publication statusPublished - 26 Jan 2004

Publication series

NameLecture Notes in Computer Science
PublisherLecture Notes in Computer Science, Springer


  • top-ranking sentences
  • TRS
  • information access
  • extraction model


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