PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents

Corina Florescu, Cornelia Caragea


Abstract
The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document’s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word’s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09%.
Anthology ID:
P17-1102
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1105–1115
Language:
URL:
https://www.aclweb.org/anthology/P17-1102
DOI:
10.18653/v1/P17-1102
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1102.pdf