@InProceedings{mahata-EtAl:2018:N18-2,
  author    = {Mahata, Debanjan  and  Kuriakose, John  and  Shah, Rajiv Ratn  and  Zimmermann, Roger},
  title     = {Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {634--639},
  abstract  = {Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.},
  url       = {http://www.aclweb.org/anthology/N18-2100}
}