Clustering Top-Ranking Sentences for Information Access
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.
- Clustering Top-Ranking Sentences for Information Access
- Book Title
- Research and Advanced Technology for Digital Libraries
- Book Subtitle
- 7th European Conference, ECDL 2003 Trondheim, Norway, August 17-22, 2003 Proceedings
- pp 523-528
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
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- Editor Affiliations
- 4. NetLab, Knowledge Technologies Group, Lund University Libraries
- 5. Dept. of Computer and Information Science, Norwegian University of Science and Technology
- Author Affiliations
- 6. Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, U.K.
- 7. Department of Computer and Information Sciences, University of Strathclyde, Glasgow, G1 1XH, U.K.
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