@InProceedings{florescu-caragea:2017:Long,
  author    = {Florescu, Corina  and  Caragea, Cornelia},
  title     = {PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1105--1115},
  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 $29.09\%$.},
  url       = {http://aclweb.org/anthology/P17-1102}
}