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Approches neuronales pour le résumé abstractif de transcriptions de parole

Abstract : In this thesis, we study the application of Deep Learning Neural Approaches for abstractive summarization for meetings reports generation. This work takes place in a context where Deep Learning is omnipresent in the Natural Language Processing field (NLP). In fact, neural models constitute the current state-of-the-art in different language generation tasks such as Machine Translation and Abstractive Summarization. However, the application of automatic summarization for meeting report generation in french remains unexplored. Indeed, this task suffers from a lack of available data because of difficulties to collect and annotate such data. In this context, our first contribution consists of the creation of a dataset for this task by aligning meeting reports with automatic transcriptions of the meeting's audio recording. We propose a methodology associating automatic alignment with human alignment. This methodology enables us to develop automatic alignment models thanks to the annotation of an evaluation dataset while facilitating the human annotation task thanks to the use of automatic pre-alignments. Then, in order to avoid constraints from the annotation -- even automatic -- we suggest running a self-supervised pre-training in order to take profit from large amounts of unaligned data. Moreover, we introduce back-summarization that allows us to generate synthetic data and create training pairs from unaligned meeting reports. We also combine those two approaches and show their synergy. In this thesis, we focus our work on the abstractive approach of automatic summarization which consists in generating a summary from scratch, as opposed to the extractive approach where parts of the source document are selected to form the summary. Indeed, writing meeting reports from automatic transcriptions requires rephrasing what is being said, optionally correcting it or reorganizing it in order to go from a spoken language to a written, and more formal language. In order to alleviate this bias, we introduce the explicit learning of the expected copy rate with control tokens. Finally, we conclude this thesis work with a human evaluation of automatic reports. This evaluation allows us to give a critical look at our models' performances as well as our experimental setup in particular on the metrics and the data used during evaluation.
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Contributor : Paul Tardy Connect in order to contact the contributor
Submitted on : Monday, September 20, 2021 - 3:47:30 PM
Last modification on : Friday, March 25, 2022 - 3:58:42 AM
Long-term archiving on: : Tuesday, December 21, 2021 - 6:02:30 PM


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  • HAL Id : tel-03259468, version 1


Paul Tardy. Approches neuronales pour le résumé abstractif de transcriptions de parole. Informatique et langage [cs.CL]. Le Mans Université, 2021. Français. ⟨tel-03259468v1⟩



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