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A Study on Automatic Keyphrase Extraction and Its Refinement for Scientific Articles

  • Yeonsoo Lim
  • Daehyeon Bong
  • Yuchul JungEmail author
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11609)

Abstract

Keyphrase extraction is a fundamental, but very important task in NLP that map documents to a set of representative words/phrases. However, state-of-the-art results on benchmark datasets are still immature stage. As an effort to alleviate the gaps between human annotated keyphrases and automatically extracted ones, in this paper, we introduce our on-going work about how to extract meaningful keyphrases of scientific research articles. Moreover, we investigate several avenues of refining the extracted ones using pre-trained word embeddings and its variations. For the experiments, we use two different datasets (i.e., WWW and KDD) in computer science domain.

Keywords

Keyphrase extraction Word embedding Refinement Scientific articles 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kumoh National Institute of Technology (KIT)GumiSouth Korea

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