Citation Count Prediction Using Abstracts
Researchers are expected to find previous literature that is related to their research and potentially has a scientific impact from among a large number of publications. This paper addresses the problem of predicting the citation count of each research paper, that is, the number of citations from other papers to that paper. Previous literature related to the problem claims that the textual data of papers do not deeply affect the prediction compared with data about the authors and venues of publication. In contrast, the authors of this paper detected the citation counts of papers using only the paper abstracts. Additionally, they investigated the effect of technical terms used in the abstracts on the detection. They classified abstracts of papers with high and low citation counts and applied the classification to the abstracts modified by hiding the technical terms used in them. The results of their experiments indicate that the high and low of citation counts of research papers can be detected using their abstracts, and the effective features used in the prediction are related to the trend of research topics.
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MeSH: Medical Subject Headings. https://www.nlm.nih.gov/ mesh/. Accessed Feb. 5, 2018.
PNAS: Proceedings of the National Academy of Sciences. http://www.pnas.org/. Accessed Feb. 5, 2018.
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