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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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
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
Boehmer J (2009) Harvard study shows face-to-face meeting value, rising virtual interest. Meeting News 33:9
Armfield R (2010) Virtual meetings save real money. Bank Technology News 23(7):13
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
Berger AL, Pietra VJD, Pietra SAD (1996) A maximum entropy approach to natural language processing. Computational Linguistics 22(1):39–71
Crabtree BF, Miller WF (1992) A template approach to text analysis: developing and using codebooks
Gupta V, Lehal GS (2010) A survey of text summarization extractive techniques. Journal of Emerging Technologies in Web Intelligence 2(3):258–268
Hovy E (2003) Text summarization. In: The Oxford Handbook of Computational Linguistics 2nd edition
Jurafsky D (2000) Speech and language processing: An introduction to natural language processing. Computational Linguistics, and Speech Recognition
Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1):1–167
Manning CD, Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT press, Cambridge
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
Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Foundations and TrendsOR in Information Retrieval 2(1–2):1–135
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
Salton G, Singhal A, Mitra M, Buckley C (1997) Automatic text structuring and summarization. Inf Process Manag 33(2):193–207
Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: Liwc and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54
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
Winograd T (1972) Understanding natural language. Cogn Psychol 3(1):1–191
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
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
Erkan G, Radev DR (2004) Lexrank: Graph-based lexical centrality as salience in text summarization. J Artif Intell Res 22:457–479
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
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
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
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117
Deng L, Yu D et al (2014) Deep learning: methods and applications. Foundations and TrendsOR in Signal Processing 7(3–4):197–387
Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press USA
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
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
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
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
Srikanth H, Denner G, Hammer MFM, Murray SR (2012) Meeting agenda management. US Patent 8,214,748
Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv preprint arXiv:180205365
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
Landauer TK, Foltz PW, Laham D (1998) An introduction to latent semantic analysis. Discourse Process 25(2–3):259–284
Mihalcea R, Tarau P (2004) Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing
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
Lin CY (2004) Rouge: A package for automatic evaluation of summaries. Text Summarization Branches Out
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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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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DOI: https://doi.org/10.1007/s11036-019-01310-x