Automatic Detection of Survey Articles

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.

• Type: Conference
• Year: 2005
• Where: ERCIMDL
• Authors: Hidetsugu Nanba, Manabu Okumura
• Keywords: ERCIMDL 2005, Important Papers, Outstanding Hub Scores, Survey Articles