Skip to main content

Summarization of Documents by Finding Key Sentences Based on Social Network Analysis

  • Conference paper
  • First Online:
Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

Abstract

Finding key sentences or paragraphs from a document is an important and challenging problem. In recent years, the amount of text data has grown astronomically and this growth has produced a great demand for text summarization. In the present study, we propose a new text summarization process by text mining and social network methods. To demonstrate the applicability of the proposed summarization procedure, we used Martin Luther King, Jr’s public speech

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Mani, I., Maybury, M.T.: Advances in automatic text summarization, vol. 293. MIT press, Cambridge (1999)

    Google Scholar 

  2. Hemp, P.: Death by information overload. Harvard business review 87(9), 82–89 (2009)

    Google Scholar 

  3. Pera, M.S., Ng, Y.K.: A Naive Bayes classifier for web document summaries created by using word similarity and significant factors. International Journal on Artificial Intelligence Tools 19(04), 465–486 (2010)

    Article  Google Scholar 

  4. Bhargavi, P., Jyothi, B., Jyothi, S., Sekar, K.: Knowledge Extraction Using Rule Based Decision Tree Approach. IJCSNS International Journal of Computer Science and Network 296 Security, pp. 296–301 (2008)

    Google Scholar 

  5. Mu, X., Hao, W., Chen, G., Zhao, S., Jin, D.: Research based on concept maps and hidden Markov model for multi-document summary. In: 2011 4th IEEE International Conference Broadband Network and Multimedia Technology (IC-BNMT), pp. 611–614 (2011)

    Google Scholar 

  6. Conroy, J.M., O’leary, D.P.: Text summarization via hidden Markov models. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 406–407. ACM (2001)

    Google Scholar 

  7. Pourvali, M., Abadeh, M.S.: Automated text summarization base on lexicales chain and graph using of wordnet and wikipedia knowledge base. arXiv preprint arXiv:1203.3586 (2012)

    Google Scholar 

  8. Kurmi, R., Jain, P.: Text summarization using enhanced MMR technique. In: 2014 International Conference Computer Communication and Informatics (ICCCI), pp. 1–5 (2014)

    Google Scholar 

  9. Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 340–348 (2010)

    Google Scholar 

  10. Zheng, H.T., Bai, S.Z.: Graph-Based Summarization without Redundancy. In: Web Technologies and Applications, pp. 449–460 (2014)

    Google Scholar 

  11. Wasserman, S.: Social network analysis: Methods and applications, vol. 8. Cambridge university press (1994)

    Google Scholar 

  12. Chowdhury, G.: Introduction to modern information retrieval. Facet publishing (2010)

    Google Scholar 

  13. Freeman, L.C.: Centrality in social networks conceptual clarification. Social networks 1(3), 215–239 (1979)

    Article  Google Scholar 

  14. Newman, M.E., Park, J.: Why social networks are different from other types of networks. Physical Review E 68(3) (2003)

    Google Scholar 

  15. Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32(3), 245–251 (2010)

    Article  Google Scholar 

  16. Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network analysis. LNCS, vol. 3418, pp. 16–61. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Netminer 4, http://www.netminer.com

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seoung Bum Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Cho, S.G., Kim, S.B. (2015). Summarization of Documents by Finding Key Sentences Based on Social Network Analysis. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19066-2_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics