Skip to main content
SpringerLink
Log in

Holographic Lexical Chain and Its Application in Chinese Text Summarization

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Lexical chain has been widely used in many NLP areas. However, when using it for Web text summarization, especially for domain-specific text summarization, we got low accuracy results. The main reason is that traditional lexical chains only take nouns into consideration while information of other grammatical parts is missing. We introduce lexical chains of predicates and adjectives (adverbs) respectively. These three types of lexical chains together are called holographic lexical chains (HLCs), which capture most of the information included in the text. A specifically designed construction method for HLC is presented. We applied HLC method to Chinese text summarization and used machine learning methods whose features are adapted to the new method. In a comparative study of Chinese foreign trade texts, we got summarization results with accuracy of 86.88%. Our HLC construction method obtained improvements of 7.02% in accuracy than the known best methods in Chinese text summarization.

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. Alam, H., Kumar, A., Nakamura, M., et al.: Structured and unstructured document summarization: design of a commercial summarizer using lexical chains. In: ICDAR, vol. 3, pp. 1147 (2003)

    Google Scholar 

  2. Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. Adv. Autom. Text Summar. 111–121 (1999)

    Google Scholar 

  3. Brügmann, S., Bouayad-Agha, N., Burga, A., et al.: Towards content-oriented patent document processing: intelligent patent analysis and summarization. World Patent Inf. 40, 30–42 (2015)

    Article  Google Scholar 

  4. Ercan, G., Cicekli, I.: Lexical cohesion based topic modeling for summarization. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 582–592. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78135-6_50

    Chapter  Google Scholar 

  5. Feng, W.L.: Research of theme statement extraction for chinese literature based on lexical chain. Int. J. Multimedia Ubiquitous Eng. 11(6), 379–388 (2016)

    Article  Google Scholar 

  6. Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining. In: IJCAI, vol. 3, pp. 1486–1488 (2003)

    Google Scholar 

  7. Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. WordNet Electr. Lex. Database 305, 305–332 (1998)

    Google Scholar 

  8. Jarmasz, M., Szpakowicz, S.: Not as easy as it seems: automating the construction of lexical chains using Roget’s Thesaurus. In: Xiang, Y., Chaib-draa, B. (eds.) AI 2003. LNCS, vol. 2671, pp. 544–549. Springer, Heidelberg (2003). doi:10.1007/3-540-44886-1_48

    Chapter  Google Scholar 

  9. Krovetz, R.: More than one sense per discourse. NEC Princeton NJ Labs., Research Memorandum (1998)

    Google Scholar 

  10. Li, J., Sun, L., Kit, C., et al.: A query-focused multi-document summarizer based on lexical chains. In: Proceedings of Document Understanding Conference (2007)

    Google Scholar 

  11. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)

    Google Scholar 

  12. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  13. Morris, J., Hirst, G.: Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Comput. Linguist. 17(1), 21–48 (1991)

    Google Scholar 

  14. Munot, N., Govilkar, S.S.: Comparative study of text summarization methods. Int. J. Comput. Appl. 102(12), 33–37 (2014)

    Google Scholar 

  15. Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 43–76. Springer, US (2012)

    Chapter  Google Scholar 

  16. Novischi, A., Moldovan, D.: Question answering with lexical chains propagating verb arguments. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 897–904. Association for Computational Linguistics (2006)

    Google Scholar 

  17. Qian, T., Ji, D., Zhang, M., et al.: Word sense induction using lexical chain based hypergraph model. In: COLING, pp. 1601–1611 (2014)

    Google Scholar 

  18. Remus, S., Biemann, C.: Three knowledge-free methods for automatic lexical chain extraction. In: HLT-NAACL, pp. 989–999 (2013)

    Google Scholar 

  19. Silber, H.G., McCoy, K.F.: Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Comput. Linguist. 28(4), 487–496 (2002)

    Article  Google Scholar 

  20. Somasundaran, S., Burstein, J., Chodorow, M.: Lexical chaining for measuring discourse coherence quality in test-taker essays. In: COLING, pp. 950–961 (2014)

    Google Scholar 

  21. Che, W., Li, Z., Liu, T.: LTP: a chinese language technology platform. In: Proceedings of the Coling 2010: Demonstrations, pp 13–16, Beijing, China, August 2010

    Google Scholar 

  22. Wei, T., Lu, Y., Chang, H., et al.: A semantic approach for text clustering using WordNet and lexical chains. Expert Syst. Appl. 42(4), 2264–2275 (2015)

    Article  Google Scholar 

  23. Xiong, D., Ding, Y., Zhang, M., et al.: Lexical chain based cohesion models for document-level statistical machine translation. In: EMNLP, pp. 1563–1573 (2013)

    Google Scholar 

Download references

Acknowledgement

This work was supported by National Key Research and Development Program of China under grant 2016YFB1000902, National Natural Science Foundation of China (No. 61232015, 61472412, 61621003), Beijing Science and Technology Project: Machine Learning based Stomatology and Tsinghua-Tencent-AMSS Joint Project: WWW Knowledge Structure and its Application.

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shengluan Hou, Yu Huang, Chaoqun Fei, Shuhan Zhang & Ruqian Lu

  2. University of Chinese Academy of Sciences, Beijing, China

    Shengluan Hou, Yu Huang, Chaoqun Fei & Shuhan Zhang

  3. Key Lab of MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Ruqian Lu

Authors
  1. Shengluan Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaoqun Fei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuhan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruqian Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shengluan Hou .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hou, S., Huang, Y., Fei, C., Zhang, S., Lu, R. (2017). Holographic Lexical Chain and Its Application in Chinese Text Summarization. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63579-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation