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.
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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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