Detecting Action Items in Meetings

  • Gabriel Murray
  • Steve Renals
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5237)

Abstract

We present a method for detecting action items in spontaneous meeting speech. Using a supervised approach incorporating prosodic, lexical and structural features, we can classify such items with a high degree of accuracy. We also examine how well various feature subclasses can perform this task on their own.

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References

  1. 1.
    Carletta, J., Ashby, S., Bourban, S., Flynn, M., Guillemot, M., Hain, T., Kadlec, J., Karaiskos, V., Kraaij, W., Kronenthal, M., Lathoud, G., Lincoln, M., Lisowska, A., McCowan, I., Post, W., Reidsma, D., Wellner, P.: The AMI meeting corpus: A pre-announcement. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 28–39. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Chen, Y.-W., Lin, C.-J.: Combining SVMs with various feature selection strategies. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature extraction, foundations and applications. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Dielmann, A., Renals, S.: DBN based joint dialogue act recognition of multiparty meetings. In: Proc. of ICASSP 2007, Honolulu, USA, pp. 133–136 (2007)Google Scholar
  4. 4.
    Galley, M.: A skip-chain conditional random field for ranking meeting utterances by importance. In: Proc. of EMNLP 2006, Sydney, Australia, pp. 364–372 (2006)Google Scholar
  5. 5.
    Hsueh, P.-Y., Kilgour, J., Carletta, J., Moore, J., Renals, S.: Automatic decision detection in meeting speech. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds.) MLMI 2007. LNCS, vol. 4892. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Janin, A., Baron, D., Edwards, J., Ellis, D., Gelbart, D., Morgan, N., Peskin, B., Pfau, T., Shriberg, E., Stolcke, A., Wooters, C.: The ICSI meeting corpus. In: Proc. of IEEE ICASSP 2003, Hong Kong, China, pp. 364–367 (2003)Google Scholar
  7. 7.
    Maskey, S., Hirschberg, J.: Comparing lexial, acoustic/prosodic, discourse and structural features for speech summarization. In: Proc. of Interspeech 2005, Lisbon, Portugal, pp. 621–624 (2005)Google Scholar
  8. 8.
    Murray, G.: Using Speech-Specific Features for Automatic Speech Summarization. PhD thesis, University of Edinburgh (2007)Google Scholar
  9. 9.
    Murray, G., Renals, S.: Term-weighting for summarization of multi-party spoken dialogues. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds.) MLMI 2007. LNCS, vol. 4892, pp. 155–166. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Murray, G., Renals, S., Carletta, J.: Extractive summarization of meeting recordings. In: Proc. of Interspeech 2005, Lisbon, Portugal, pp. 593–596 (2005)Google Scholar
  11. 11.
    Purver, M., Dowding, J., Niekrasz, J., Ehlen, P., Noorbaloochi, S.: Detecting and summarizing action items in multi-party dialogue. In: Proc. of the 9th SIGdial Workshop on Discourse and Dialogue, Antwerp, Belgium (2007)Google Scholar
  12. 12.
    Purver, M., Ehlen, P., Niekrasz, J.: Detecting action items in multi-party meetings: Annotation and initial experiments. In: Renals, S., Bengio, S., Fiscus, J.G. (eds.) MLMI 2006. LNCS, vol. 4299, pp. 200–211. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Valenza, R., Robinson, T., Hickey, M., Tucker, R.: Summarization of spoken audio through information extraction. In: Proc. of the ESCA Workshop on Accessing Information in Spoken Audio, Cambridge, UK, pp. 111–116 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gabriel Murray
    • 1
  • Steve Renals
    • 2
  1. 1.University of British ColumbiaVancouverCanada
  2. 2.University of EdinburghEdinburghScotland

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