Abstract
We propose a method for detecting survey articles in a multilingual database. Generally, a survey article cites many important papers in a research domain. Using this feature, it is possible to detect survey articles. We applied HITS, which was devised to retrieve Web pages using the notions of authority and hub. We can consider that important papers and survey articles correspond to authorities and hubs, respectively. It is therefore possible to detect survey articles, by applying HITS to databases and by selecting papers with outstanding hub scores. However, HITS does not take into account the contents of each paper, so the algorithm may detect a paper citing many principal papers in mistake for survey articles. We therefore improve HITS by analysing the contents of each paper. We conducted an experiment and found that HITS was useful for the detection of survey articles, and that our method could improve HITS.
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References
Cohn, D., Chang, H.: Learning to probabilistically identify authoritative documents. In: Proceedings of the 17th International Conference on Machine Learning, pp. 167–174 (2000)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th Annual ACM–SIAM Symposium on Discrete Algorithms, pp. 668–677 (1998)
Lawrence, S., Giles, L., Bollacker, K.: Digital libraries and autonomous citation indexing. IEEE Computer 32(6), 67–71 (1999)
McCallum, A., Nigam, K., Rennie, J., Seymore, K.: Building domain-specific search engines with machine learning techniques. In: Proceedings of AAAI 1999 Spring Symposium on Intelligent Agents in Cyberspace (1999)
Nanba, H., Okumura, M.: Towards multi-paper summarization using reference information. In: Proceedings of the 16th International Joint Conferences on Artificial Intelligence, pp. 926–931 (1999)
Sparck Jones, K., Van Rijsbergen, C.J.: Report on the need for and provision of ‘ideal’ test collections. In: British Library Research and Development Report 5266. Computer Laboratory, University of Cambridge (1975)
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© 2005 Springer-Verlag Berlin Heidelberg
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Nanba, H., Okumura, M. (2005). Automatic Detection of Survey Articles. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2005. Lecture Notes in Computer Science, vol 3652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551362_35
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DOI: https://doi.org/10.1007/11551362_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28767-4
Online ISBN: 978-3-540-31931-3
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