Conundrums in unsupervised keyphrase extraction: making sense of the state-of-the-art

Authors:
Kazi Saidul Hasan;Vincent Ng
Affiliations:
University of Texas at Dallas;University of Texas at Dallas
Venue:
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Year:
2010

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Abstract

State-of-the-art approaches for unsupervised keyphrase extraction are typically evaluated on a single dataset with a single parameter setting. Consequently, it is unclear how effective these approaches are on a new dataset from a different domain, and how sensitive they are to changes in parameter settings. To gain a better understanding of state-of-the-art unsupervised keyphrase extraction algorithms, we conduct a systematic evaluation and analysis of these algorithms on a variety of standard evaluation datasets.