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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Leuski, A., Allan, J.: Evaluating a visual navigation system for a digital library. In: Nikolaou, C., Stephanidis, C. (eds.) ECDL 1998. LNCS, vol. 1513, p. 535. Springer, Heidelberg (1998)
Lopez, M.J.M., Rodriguez, M.B., Hidalgo, J.M.G.: Using and evaluating user directed summaries to improve information access. In: Abiteboul, S., Vercoustre, A.-M. (eds.) ECDL 1999. LNCS, vol. 1696, pp. 198–214. Springer, Heidelberg (1999)
Willett, P.: Recent trends in hierarchic document clustering: a critical review. Information Procsessing & Management 24, 577–597 (1988)
White, R.W., Ruthven, I., Jose, J.M.: A task-oriented study on the influencing effects of query-biased summarisation in web searching. Information Procsessing & Management in press (2003)
Zamir, O., Etzioni, O.: Web document clustering: A feasibility demonstration. In: Proceedings of the 21st Annual ACM SIGIR Conference, Melbourne, Australia, pp. 46–54 (1998)
White, R.W., Ruthven, I., Jose, J.M.: Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes. In: Proceedings of the 24th Annual ACM SIGIR Conference, Tampere, Finland, pp. 57–64 (2002)
Radev, D.R., Jing, H., Budzikowska, M.: Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In: Proceedings of the ANLP/NAACL Workshop on Summarization, Seattle, U.S.A (2000)
Zha, H.: Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering. In: Proceedings of the 25th Annual ACM SIGIR Conference, Tampere, Finland, pp. 113–120 (2002)
Kural, Y., Robertson, S.E., Jones, S.: Deciphering cluster representations. Information Procsessing & Management 37, 593–601 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. https://doi.org/10.1007/978-3-540-45175-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-45175-4_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40726-3
Online ISBN: 978-3-540-45175-4
eBook Packages: Springer Book Archive