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

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


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.


Keyphrase extraction Word embedding Refinement Scientific articles 


  1. 1.
    Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: Proceedings of the 23rd National Conference on Artificial Intelligence, pp. 855–860 (2008)Google Scholar
  2. 2.
    Mothe, J., Rasolomanana, M.: Automatic keyphrase extraction using graph-based methods. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 728–730 (2018)Google Scholar
  3. 3.
    Bennani-Smires, K., Musat, C., Hossmann, A., Baeriswyl, M., Jaggi, M.: Simple unsupervised keyphrase extraction using sentence embeddings. In: Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL), pp. 221–229 (2018)Google Scholar
  4. 4.
    Mahata, D., Kuriakose, J., Shah, R.R., Zimmermann, R.: Key2Vec: automatic ranked keyphrase extraction from scientific articles using phrase embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 634–639 (2018)Google Scholar
  5. 5.
    Diaz, F., Mitra, B., Craswell, N.: Query expansion with locally-trained word embeddings. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 367–377 (2016)Google Scholar
  6. 6.
    Yu, L.-C., Wang, J., Lai, K.R., Zhang, X.: Refining word embeddings for sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 534–539 (2017)Google Scholar
  7. 7.
    Liu, Q., Huang, H., Gao, Y., Wei, X., Tian, Y., Liu, L.: Task-oriented word embedding for text classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2023–2032 (2018)Google Scholar
  8. 8.
    Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., Jatowt, A.: YAKE! collection-independent automatic keyword extractor. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 806–810. Springer, Cham (2018). Scholar
  9. 9.
    Caragea, C., Bulgarov, F.A., Godea, A., Das Gollapalli, S.: Citation-enhanced keyphrase extraction from research papers: a supervised approach. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1435–1446 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

Personalised recommendations