Automatic Extraction of Task Statements from Structured Meeting Content

Katashi Nagao, Kei Inoue, Naoya Morita, Shigeki Matsubara

2015

Abstract

We previously developed a discussion mining system that records face-to-face meetings in detail, analyzes their content, and conducts knowledge discovery. Looking back on past discussion content by browsing documents, such as minutes, is an effective means for conducting future activities. In meetings at which some research topics are regularly discussed, such as seminars in laboratories, the presenters are required to discuss future issues by checking urgent matters from the discussion records. We call statements including advice or requests proposed at previous meetings “task statements” and propose a method for automatically extracting them. With this method, based on certain semantic attributes and linguistic characteristics of statements, a probabilistic model is created using the maximum entropy method. A statement is judged whether it is a task statement according to its probability. A seminar-based experiment validated the effectiveness of the proposed extraction method.

References

  1. Sellen, A. J. and Whittaker, S., 2010, Beyond Total Capture: A Constructive Critique of Lifelogging. Commun. ACM, vol. 53, no. 5, pp. 70-77.
  2. Mayer-Schönberger, V., Cukier, K., 2013. Big Data: A Revolution that Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt.
  3. Nagao, K., Kaji, K., Yamamoto D. and Tomobe, H., 2005. Discussion Mining: Annotation-Based Knowledge Discovery from Real World Activities, Advances in Multimedia Information Processing - PCM 2004, LNCS, vol. 3331, pp. 522-531. Springer.
  4. Wu, N., 1997. The Maximum Entropy Method, Springer Series in Information Sciences, 32, Springer.
  5. Kanayama, H. and Nasukawa, T., 2008. Textual Demand Analysis: Detection of User's Wants and Needs from Opinions, In Proceedings of the 22nd International Conference on Computational Linguistics (COLING2008), pp. 409-416.
  6. Yang, W.-Y., Cao, Y. and Lin, C.-Y., 2009, A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 514-523.
  7. Sarencheh, S., Potdar, V., Yeganeh, E. A. and Firoozeh, N., 2010, Semi-automatic Information Extraction from Discussion Boards with Applications for Anti-Spam Technology, In Proceedings of ICCSA 2010, LNCS, vol. 6017, pp. 370-382. Springer.
  8. Qu, Z. and Liu, Y., 2012, Sentence Dependency Tagging in Online Question Answering Forums, In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 554-562.
  9. Wicaksono, A. F. and Myaeng, S.-H., 2013, Automatic Extraction of Advice-revealing Sentences for Advice Mining from Online Forums, In Proceedings of the 7th International Conference on Knowledge Capture (KCAP 2013).
  10. Conklin, J. and Begeman, M. L., 1988. gIBIS: A Hypertext Tool for Exploratory Policy Discussion, ACM Transactions on Information Systems (TOIS), vol. 6, no. 4, pp. 140-152.
  11. Rienks, R., Heylen, D., and van der Weijden, 2005. Argument Diagramming of Meeting Conversations, In Proceedings of the Multimodal Multiparty Meeting Processing Workshop at the 7th International Conference on Multimodal Interfaces (ICMI 2005).
  12. Lee, D., Erol, B., Graham, J., Hull, J. and Murata, N., 2002. Portable Meeting Recorder, In Proceedings of ACM Multimedia 2002, pp. 493-502.
  13. Good, P., 1994. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypothesis, Springer.
  14. Tsuchida, T., Ohira, S. and Nagao, K., 2008. Knowledge Activity Support System Based on Discussion Content, In Proceedings of the Fourth International Conference on Collaboration Technologies.
Download


Paper Citation


in Harvard Style

Nagao K., Inoue K., Morita N. and Matsubara S. (2015). Automatic Extraction of Task Statements from Structured Meeting Content . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 307-315. DOI: 10.5220/0005609703070315


in Bibtex Style

@conference{kdir15,
author={Katashi Nagao and Kei Inoue and Naoya Morita and Shigeki Matsubara},
title={Automatic Extraction of Task Statements from Structured Meeting Content},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={307-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005609703070315},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Automatic Extraction of Task Statements from Structured Meeting Content
SN - 978-989-758-158-8
AU - Nagao K.
AU - Inoue K.
AU - Morita N.
AU - Matsubara S.
PY - 2015
SP - 307
EP - 315
DO - 10.5220/0005609703070315