A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions
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Abstract
In this paper, we present a time series structure analysis method of a meeting using text data and a method for the visualization of state transitions. Our method evaluates and visualizes the convergence/divergence of the meeting in a time series using text data from the meeting. It is important to facilitate and review meetings for improving efficiency. Therefore, it is important not only to review the final agreement and conclusion in the dialogue during the meeting but also to understand the dialogue process. We introduce two indicators: freshness and representativeness. Our system expresses the status of the meeting in four quadrants (“stagnation”, “exploration”, “deepening”, and “consensus building”) corresponding to the combination of degrees of freshness and representativeness. By conducting an analysis using these indicators, we can objectively find which parts of the dialogue stagnated or advanced the discussion. In addition, it is possible to clarify the meeting process as a structure for review and facilitation, thereby improving efficiency of the meeting. Thus, we implemented a system that realizes this method. Furthermore, we applied this system to real data gathered from meetings consisting of actual multi-company members and verified its effectiveness.
Keywords
Meeting support Divergence and convergence Verbal speech information Time series visualizationNotes
Acknowledgements
We wish to thank Kunihiko Ueshima (Japan Data Exchange Inc.) for his cooperation with the questionnaire.
References
- 1.NTT DATA Institute of Management Consulting. Survey on ‘Conference innovation and work style’. https://www.keieiken.co.jp/aboutus/newsrelease/121005/. Accessed 23 Mar 2017 (in Japanese)
- 2.Cameron: Working with Spoken Discourse. SAGE Publications Ltd., Thousand Oaks (2001)Google Scholar
- 3.Okada, R., Nakanishi, T., Tanaka, Y., Ogasawara, Y., Ohashi, K.: A topic structuration method on time series for a meeting from text data. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2017. Studies in Computational Intelligence, vol. 721, pp. 45–59. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62048-0_4
- 4.Bergstrom, T., Karahalios, K.: Conversation clock: visualizing audio patterns in co-located groups. In: Proceedings of the Hawaii International Conference on System Sciences (HICSS 2007), pp. 1317–1325 (2007)Google Scholar
- 5.Ichino, J., Tano, S.: Some characteristics of utterance patterns for discriminating phases of meetings. Inst. Electron. Inf. Commun. Eng. (IEICE) Tech. Rep. Human Commun. Sci. (HCS) 109(224), 23–28 (2009)Google Scholar
- 6.Olguin, D.O., Waber, B.N., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible organizations: technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man. Cybern. Part B (Cybernetics) 39(1), 43–55 (2009)CrossRefGoogle Scholar
- 7.Guilford, J.P.: The Nature of Human Intelligence. McGraw-Hill, New York (1967)Google Scholar
- 8.Osborn, A.F.: Applied imagination. Principles and procedures of creative problem-solving. Charles Scribner’s Sons, New York (1953)Google Scholar
- 9.Kawakita, J.: Hasso-ho (Method for Making Ideas). Chuko Shinsho, Tokyo (1996) (in Japanese)Google Scholar
- 10.Hori, K.: Fashiritēshon Nyūmon (Introduction to facilitation). Nikkei Publishing Inc., Tokyo (2004) (in Japanese)Google Scholar
- 11.Ichino, J., Tano, S.: Discrimination of phases of meetings from utterance patterns in group meetings. Jpn. Soc. Artif. Intell. 25(3), 504–513 (2010)Google Scholar
- 12.Tomiyama, K., Nihei, F., Nakano, Y., Takase, Y.: Classifying divergent/convergent utterances in group discussions based on verbal and nonverbal. In: The 31st National Convention of the Japanese Society for Artificial Intelligence, 2H4-OS-35b-5 (2017) (in Japanese)Google Scholar
- 13.Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Computat. Linguist. 23(1), 33–64 (1997)Google Scholar