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Korean Document Summarization Using Topic Phrases Extraction and Locality-Based Similarity

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2871))

Abstract

We describe a hybrid approach to summarize the document content using the topic phrases extraction and the query-based summary. The topic phrases are extracted using machine learning algorithm. We use these topic phrases as the query terms with locality-based similarity calculation in order to extract highly ranked sentences or paragraph. We experiment with three machine learning methods, Naive Bayesian, decision tree and supported vector machine, for extracting the topic phrases effectively and discuss the results. The overall summaries have been evaluated for the extraction accuracy compared with the human-selected summaries.

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References

  1. de Kretser, O., Moffat, A.: Needles and Haystacks: A Search Engine for Personal Information Collections. In: de Krester, M. (eds.) Proc. 23rd Australasian Computer Science Conference, Canberra, January 2000, pp. 58–65 (2000)

    Google Scholar 

  2. Euler, T.: Tailoring Text Using Topic Words: Selection and Compression. In: Proceedings of the 13th International Workshop on Database and Expert Systems Applications (DEXA). IEEE Computer Society Press, Los Alamitos (2002)

    Google Scholar 

  3. Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-specific keyphrase extraction. In: Proc. Int. Joint Conf. on Artificial Intelligence, Stockholm, Sweden, pp. 668–673 (1999)

    Google Scholar 

  4. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: Practical Automatic Keyphrase Extraction. ACM DL, 254–255 (1999)

    Google Scholar 

  5. Pazzani, M., Nguyen, L., Mantik, S.: Learning from hotlists and coldlists: Towards a WWW information filtering and seeking agent, http://www.ics.uci.edu/~pazzani/Coldlist.html

  6. Mathieu, J.: Adaptation of a Keyphrase Extractor for Japanese Text, http://extractor.iit.nrc.ca/reports/CAIS99.ps.Z

  7. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: Practical Automatic Keyphrase Extracting. ACM DL, 254–255 (1999)

    Google Scholar 

  8. de Kretser, O., Moffat, A.: Needles and Haystacks: A Search Engine for Personal Information Collections. In: de Kretser, M. (ed.) Proc. 23rd Australasian Computer Science Conference, Canberra, January 2000, pp. 58–65 (2000)

    Google Scholar 

  9. Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., Nevill-Manning, C.G.: Domain-Specific Keyphrase Extraction. In: Proc. Sixteenth International Joint Conference on Artificial Intelligence, pp. 668–673. Morgan Kaufmann Publishers, San Francisco (1999)

    Google Scholar 

  10. Kan, M.-Y., McKeown, K.R., Klavans, J.L.: Domain-specific informative and indicative summarization for information retrieval. In: Proceedings of the Document Understanding Workshop (DUC 2001), New Orleans, USA (September 2001)

    Google Scholar 

  11. Chen, F.R., Dan Bloomberg, S.: Extraction of Inductive Summary Sentences from Imaged Documents. In: ICDAR 1997, August 1997, pp. 227–232 (1997)

    Google Scholar 

  12. Kan, M.-Y., McKeown, K.R., Klavans, J.L.: Applying Natural Language Generation to Indicative Summarization. In: Proceedings of 8th European Workshop on Natural Language Generation, Toulouse, France, July 2001, pp. 92–100 (2001)

    Google Scholar 

  13. Ryu, J., Han, K.-R., et al.: Automatic Extraction of Core Sentences from Document. In: ICEIC 2000 (August 2000)

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Ryu, J., Han, KR., Rim, KW. (2003). Korean Document Summarization Using Topic Phrases Extraction and Locality-Based Similarity. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_44

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  • DOI: https://doi.org/10.1007/978-3-540-39592-8_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20256-1

  • Online ISBN: 978-3-540-39592-8

  • eBook Packages: Springer Book Archive

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