Abstract
A lot of research has been conducted all over the world in the domain of automatic text summarization and more specifically using machine learning techniques. Many state of the art prototypes partially solve this problem so we decided to use some of them to build a tool for automatic generation of meeting minutes. In fact, this was not an easy work and this paper presents various experiments that we did using Deep Learning, GANs and Transformers to achieve this goal as well as dead ends we have encountered during this study. We think providing such a feedback may be useful to other researchers who would like to undertake the same type of work to allow them to know where to go and where not to go.
This work has been carried out as part of the REUS project funded under the FUI 22 by BPI France, the Auvergne Rhône-Alpes Region and the Grenoble metropolitan area, with the support of the competitiveness clusters Minalogic, Cap Digital and TES.
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Notes
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More details can be found here: http://opennmt.net/OpenNMT-py/Summarization.html.
References
Carletta, J., et al.: The AMI meeting corpus: a pre-announcement. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 28–39. Springer, Heidelberg (2006). https://doi.org/10.1007/11677482_3
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171–4186 (2019)
Gehrmann, S., Deng, Y., Rush, A.M.: Bottom-up abstractive summarization. In: Proceedings of the Conference on Empirical Methods in Natural, pp. 4098–4109 (2018)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Proceedings of the Conference on Neural Information Processing Systems, pp. 2672–2680 (2014)
Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., Wang, J.: Long text generation via adversarial training with leaked information. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 5141–5148. AAAI Press (2018)
Gupta, S., Gupta, S.K.: Abstractive summarization: an overview of the state of the art. Expert Syst. Appl. 121, 49–65 (2019)
Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Proceedings of the Conference on Neural Information Processing Systems, pp. 1693–1701 (2015)
Jones, K.S.: Automatic summarising: the state of the art. Inf. Process. Manag. 43(6), 1449–1481 (2007)
Lin, C., Hovy, E.H.: Automatic evaluation of summaries using N-gram co-occurrence statistics. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 150–157. ACL (2003)
Liu, Y., Lapata, M.: Text summarization with pretrained encoders. In: Proceedings of the Conference on Empirical Methods in Natural, pp. 3728–3738 (2019)
Lloret, E., Palomar, M.: Text summarisation in progress: a literature review. Artif. Intell. Rev. 37(1), 1–41 (2012)
Nallapati, R., Zhou, B., dos Santos, C.N., Gülçehre, Ç., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the Conference on Computational Natural Language Learning, pp. 280–290 (2016)
Napoles, C., Gormley, M.R., Durme, B.V.: Annotated Gigaword. In: Workshop Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, pp. 95–100 (2012)
Over, P., Dang, H., Harman, D.: DUC in context. Inf. Process. Manag. 43(6), 1506–1520 (2007)
Saggion, H., Poibeau, T.: Automatic text summarization: past, present and future. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds.) Multi-source, Multilingual Information Extraction and Summarization, pp. 3–21. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-28569-1_1
See, A., Liu, P., Manning, C.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 1073–1083 (2017)
Wieting, J., Gimpel, K.: ParaNMT-50M: pushing the limits of paraphrastic sentence embeddings with millions of machine translations. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 451–462 (2018)
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Doan, T.M., Jacquenet, F., Largeron, C., Bernard, M. (2020). A Study of Text Summarization Techniques for Generating Meeting Minutes. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_33
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