Skip to main content
SpringerLink
Log in

PTR: Phrase-Based Topical Ranking for Automatic Keyphrase Extraction in Scientific Publications

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

Included in the following conference series:

Abstract

Automatic keyphrase extraction plays an important role for many information retrieval (IR) and natural language processing (NLP) tasks. Motivated by the facts that phrases have more semantic information than single words and a document consists of multiple semantic topics, we present PTR, a phrase-based topical ranking method for keyphrase extraction in scientific publications. Candidate keyphrases are divided into different topics by LDA and used as vertices in a phrase-based graph of the topic. We then decompose PageRank into multiple weighted-PageRank to rank phrases for each topic. Keyphrases are finally generated by selecting candidates according to their overall scores on all related topics. Experimental results show that PTR has good performance on several datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nguyen, T.D., Kan, M.-Y.: Keyphrase extraction in scientific publications. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 317–326. Springer, Heidelberg (2007)

    Google Scholar 

  2. Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of Association for Computational Linguistics (ACL). Association for Computational Linguistics, Baltimore, Maryland (2014)

    Google Scholar 

  3. Mihalcea, R., Tarau P.: TextRank: bringing order into texts. Association for Computational Linguistics (2004)

    Google Scholar 

  4. Liu, Z., Huang, W., Zheng, Y., et al.: Automatic keyphrase extraction via topic decomposition. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 366–376. Association for Computational Linguistics (2010)

    Google Scholar 

  5. Wan, X., Yang, J., Xiao, J.: Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction. In: Annual Meeting-Association for Computational Linguistics. vol. 45, no. 1, p. 552 (2007)

    Google Scholar 

  6. Tomokiyo, T., Hurst, M.: A language model approach to keyphrase extraction. In: Proceedings of ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, vol. 18, pp. 33–40. Association for Computational Linguistics (2003)

    Google Scholar 

  7. Bougouin, A., Boudin, F., Topicrank, D.B.: Graph-based topic ranking for keyphrase extraction. In: International Joint Conference on Natural Language Processing (IJCNLP), pp. 543–551 (2013)

    Google Scholar 

  8. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  9. Barker, K., Cornacchia, N.: Using noun phrase heads to extract document keyphrases. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 40–52. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  10. Kim, S.N., Medelyan, O., Kan, M.Y. Semeval- task 5: automatic keyphrase extraction from scientific articles. In: Proceedings of 5th International Workshop on Semantic Evaluation, pp. 21–26. Association for Computational Linguistics (2010)

    Google Scholar 

  11. Krapivin, M., Autaeu, A., Marchese, M.: Large dataset for keyphrases extraction (2009)

    Google Scholar 

  12. Hulth, A.: Improved automatic keyword extraction given more linguistic knowledge. In: Proceedings of conference on Empirical Methods in Natural Language Processing, pp. 216–223. Association for Computational Linguistics (2003)

    Google Scholar 

Download references

Acknowledgments

This work was supported by China NSF Grants (No. 61572250 and No. 61223003) and Jiangsu Province Industry Support Program (BE2014131).

Author information

Authors and Affiliations

  1. The National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Minmei Wang, Bo Zhao & Yihua Huang

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Minmei Wang, Bo Zhao & Yihua Huang

Authors
  1. Minmei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yihua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yihua Huang .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Wang, M., Zhao, B., Huang, Y. (2016). PTR: Phrase-Based Topical Ranking for Automatic Keyphrase Extraction in Scientific Publications. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46681-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation