Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Using top-ranking sentences to facilitate effective information access: Book Reviews
Journal of the American Society for Information Science and Technology
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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This paper presents two sentence retrieval methods. We adopt the task definition done in the TREC Novelty Track: sentence retrieval consists in the extraction of the relevant sentences for a query from a set of relevant documents for that query. We have compared the performance of the Latent Semantic Indexing (LSI) retrieval model against the performance of a topic identification method, also based on Singular Value Decomposition (SVD) but with a different sentence selection method. We used the TREC Novelty Track collections from years 2002 and 2003 for the evaluation. The results of our experiments show that these techniques, particularly sentence retrieval based on topic identification, are valid alternative approaches to other more ad-hoc methods devised for this task.