Abstract
Document similarity search is to find documents similar to a query document in a text corpus and return a ranked list of documents to users, which is widely used in recommender systems in library or web applications. The popular approach to similarity search is to calculate the similarities between the query document and documents in the corpus and then rank the documents. In this paper, we investigate the use of document summarization techniques to improve the effectiveness of document similarity search. In the proposed summary-based approach, the query document is summarized and similarity searches are performed with the new query of the produced summary instead of the original document. Different retrieval models and different summarization methods are investigated in the experiments. Experimental results demonstrate the higher effectiveness of the summary-based similarity search.
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References
Aggarwal, C.C., Yu, P.S.: On effective conceptual indexing and similarity search in text data. In: Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, California, USA, pp. 3–10 (2001)
Brants, T., Stolle, R.: Finding similar documents in document collections. In: Proceedings of the Third International Conference on Language Resources and Evaluation (LREC 2002), Workshop on Using Semantics for Information Retrieval and Filtering, Las Palmas, Spain (2002)
Carbonell, J., Goldstein, J.: The Use of MMR, Diversity-based Reranking for Reordering Documents and Producing Summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336 (1998)
Kolcz, A., Prabakarmurthi, V., Kalita, J.: Summarization as feature selection for text categorization. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 365–370 (2001)
Lam-Adesina, A.M., Jones, G.J.F.: Applying summarization techniques for term selection in relevance feedback. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–9 (2001)
Sakai, T., Jones, K.S.: Generic summaries for indexing in information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 190–198 (2001)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wan, X., Yang, J. (2005). Document Similarity Search Based on Generic Summaries. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_60
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DOI: https://doi.org/10.1007/11562382_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29186-2
Online ISBN: 978-3-540-32001-2
eBook Packages: Computer ScienceComputer Science (R0)