Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Towards multi-paper summarization reference information
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Blind men and elephants: What do citation summaries tell us about a research article?
Journal of the American Society for Information Science and Technology
Comparing citation contexts for information retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Temporal reasoning system for the digital theater library
IMSA '07 Proceedings of the Eleventh IASTED International Conference on Internet and Multimedia Systems and Applications
Document clustering of scientific texts using citation contexts
Information Retrieval
Rational Research model for ranking semantic entities
Information Sciences: an International Journal
Scientometrics
Improving MeSH classification of biomedical articles using citation contexts
Journal of Biomedical Informatics
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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.