Clustering Top-Ranking Sentences for Information Access
- 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
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.
Unable to display preview. Download preview PDF.