@InProceedings{teneva-cheng:2017:Short,
  author    = {Teneva, Nedelina  and  Cheng, Weiwei},
  title     = {Salience Rank: Efficient Keyphrase Extraction with Topic Modeling},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {530--535},
  abstract  = {Topical PageRank (TPR) uses latent topic distribution inferred by Latent
	Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from
	documents. The ranking procedure consists of running PageRank K times, where K
	is the number of topics used in the LDA model. In this paper, we propose a
	modification of TPR, called Salience Rank. Salience Rank only needs to run
	PageRank once and extracts comparable or better keyphrases on benchmark
	datasets. In addition to quality and efficiency benefit, our method has the
	flexibility to extract keyphrases with varying tradeoffs between topic
	specificity and corpus specificity.},
  url       = {http://aclweb.org/anthology/P17-2084}
}