Clustering Top-Ranking Sentences for Information Access

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.

• Type: Conference
• Year: 2003
• Where: ERCIMDL
• Authors: Anastasios Tombros, Joemon M. Jose, Ian Ruthven
• Keywords: ERCIMDL 2003, Effective Information Access, Sentence Extraction Model, Top-ranking Sentences