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SmartMeeting: An Novel Mobile Voice Meeting Minutes Generation and Analysis System

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Abstract

In this paper, we focus on designing and implementing SmartMeeting, an intelligent system for generating meeting minutes from meeting audio data. SmartMeeting mainly consists of four parts: the first part is the meeting speech detection, which is used to automatically extract multiple speech segments of each speaker and then sort them by time, and mark the corresponding speakers separately in different time periods; The second part is the meeting voice text transcription, the voice segment cut in the previous step is transcribed into a complete meeting text; The third part is the extraction of the meeting minutes, extracting meaningful key phrases and abstract sentences from the complete meeting text; The fourth part is the next work plan extraction, through the emotion recognition algorithm to filter out the negative emotion summary from the multiple meeting minutes, so as to show the next work plan of the meeting. By comparing and evaluating different baseline algorithms on real-world audio meeting datasets, experiments have proven that SmartMeeting can accurately summarize meetings and analyze agreed actions.

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References

  1. Rogelberg SG, Scott CW, Agypt B, Williams J, Kello JE, McCausland T, Olien JL (2014) Lateness to meetings: Examination of an unexplored temporal phenomenon. European Journal of Work and Organizational Psychology 23(3):323–341

    Article  Google Scholar 

  2. Yoerger M, Crowe J, Allen JA (2015) Participate or else!: The effect of participation in decision-making in meetings on employee engagement. Consulting Psychology Journal: Practice and Research 67(1):65

    Article  Google Scholar 

  3. Boehmer J (2009) Harvard study shows face-to-face meeting value, rising virtual interest. Meeting News 33:9

    Google Scholar 

  4. Armfield R (2010) Virtual meetings save real money. Bank Technology News 23(7):13

    Google Scholar 

  5. Allahyari M, Pouriyeh S, Assefi M, Safaei S, Trippe ED, Gutierrez JB, Kochut K (2017) Text summarization techniques: A brief survey. arXiv preprint arXiv:170702268

  6. Berger AL, Pietra VJD, Pietra SAD (1996) A maximum entropy approach to natural language processing. Computational Linguistics 22(1):39–71

    Google Scholar 

  7. Crabtree BF, Miller WF (1992) A template approach to text analysis: developing and using codebooks

  8. Gupta V, Lehal GS (2010) A survey of text summarization extractive techniques. Journal of Emerging Technologies in Web Intelligence 2(3):258–268

    Article  Google Scholar 

  9. Hovy E (2003) Text summarization. In: The Oxford Handbook of Computational Linguistics 2nd edition

  10. Jurafsky D (2000) Speech and language processing: An introduction to natural language processing. Computational Linguistics, and Speech Recognition

  11. Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1):1–167

    Article  Google Scholar 

  12. Manning CD, Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT press, Cambridge

    MATH  Google Scholar 

  13. Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, p 271

  14. Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Foundations and TrendsOR in Information Retrieval 2(1–2):1–135

    Article  Google Scholar 

  15. Salton G, Yang CS, Yu CT (1975) A theory of term importance in automatic text analysis. J Am Soc Inf Sci 26(1):33–44

    Article  Google Scholar 

  16. Salton G, Singhal A, Mitra M, Buckley C (1997) Automatic text structuring and summarization. Inf Process Manag 33(2):193–207

    Article  Google Scholar 

  17. Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: Liwc and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54

    Article  Google Scholar 

  18. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing, Association for Computational Linguistics, pp 347–354

  19. Winograd T (1972) Understanding natural language. Cogn Psychol 3(1):1–191

    Article  Google Scholar 

  20. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, IEEE, pp. 6645–6649

  21. Hinton G, Deng L, Yu D, Dahl G, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Kingsbury B et al (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process Mag 29

  22. Erkan G, Radev DR (2004) Lexrank: Graph-based lexical centrality as salience in text summarization. J Artif Intell Res 22:457–479

    Article  Google Scholar 

  23. Schuller B, Rigoll G, Lang M (2003) Hidden Markov model-based speech emotion recognition. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings (ICASSP’03), IEEE, vol 2, pp II–1

  24. Curtis P, Gupta A, Johnson B, Drakos KJ, Hough PJ, Czerwinski MP, McAniff RJ, Ozzie RE (2012) Collaborative generation of meeting minutes and agenda confirmation. US Patent 8,266,534

  25. Thompson P, James A, Stanciu E (2010) Agent based ontology driven virtual meeting assistant. In: International Conference on Future Generation Information Technology, Springer, pp 51–62

  26. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  27. Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117

    Article  Google Scholar 

  28. Deng L, Yu D et al (2014) Deep learning: methods and applications. Foundations and TrendsOR in Signal Processing 7(3–4):197–387

    Article  MathSciNet  Google Scholar 

  29. Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press USA

  30. Tur G, Stolcke A, Voss L, Peters S, Hakkani-Tur D, Dowding J, Favre B, Fernández R, Frampton M, Frandsen M et al (2010) The calo meeting assistant system. IEEE Trans Audio Speech Lang Process 18(6):1601–1611

    Article  Google Scholar 

  31. Carletta J, Ashby S, Bourban S, Flynn M, Guillemot M, Hain T, Kadlec J, Karaiskos V, Kraaij W, Kronenthal M, et al. (2005) The ami meeting corpus: A pre-announcement. In: International Workshop on Machine Learning for Multimodal Interaction, Springer, pp. 28–39

  32. Hain T, Burget L, Dines J, Garau G, Wan V, Karafi M, Vepa J, Lincoln M (2007) The ami system for the transcription of speech in meetings. In: Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, IEEE, vol 4, pp IV–357

  33. Renals S, Hain T, Bourlard H (2007) Recognition and understanding of meetings the ami and amida projects. In: Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on, IEEE, pp 238–247

  34. Srikanth H, Denner G, Hammer MFM, Murray SR (2012) Meeting agenda management. US Patent 8,214,748

  35. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:180205365

  36. Bui T, Jones C, Heigham C, Leighton T (1989) Improving the performance of the kernighan-lin and simulated annealing graph bisection algorithms. In: 26th ACM/IEEE design automation conference, IEEE, pp 775–778

  37. Landauer TK, Foltz PW, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25(2–3):259–284

    Article  Google Scholar 

  38. Mihalcea R, Tarau P (2004) Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing

  39. Vanderwende L, Suzuki H, Brockett C, Nenkova A (2007) Beyond sumbasic: Task-focused summarization with sentence simplification and lexical expansion. Inf Process Manag 43(6):1606–1618

    Article  Google Scholar 

  40. Lin CY (2004) Rouge: A package for automatic evaluation of summaries. Text Summarization Branches Out

Download references

Acknowledgements

This work is partially supported by The National Key Research Development Program of China #2018YFB1003605, Open Fund of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences No.CARCH201704, The Youth Innovation Team of Shaanxi Universities, Shaanxi Fund 2018JM6125 and B018230008, BD34017020001, Natural Science Foundation of China (NSFC) #61472312.

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Correspondence to Junzhao Du.

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Liu, H., Liu, H., Wang, X. et al. SmartMeeting: An Novel Mobile Voice Meeting Minutes Generation and Analysis System. Mobile Netw Appl 25, 521–536 (2020). https://doi.org/10.1007/s11036-019-01310-x

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