Approches par bandit pour la génération automatique de résumés de textes

Authors: Godbout, Mathieu
Advisor: Lamontagne, Luc; Durand, Audrey
Abstract: This thesis discusses the use of bandit methods to solve the problem of training extractive abstract generation models. The extractive models, which build summaries by selecting sentences from an original document, are difficult to train because the target summary of a document is usually not built in an extractive way. It is for this purpose that we propose to see the production of extractive summaries as different bandit problems, for which there exist algorithms that can be leveraged for training summarization models.In this paper, BanditSum is first presented, an approach drawn from the literature that sees the generation of the summaries of a set of documents as a contextual bandit problem. Next,we introduce CombiSum, a new algorithm which formulates the generation of the summary of a single document as a combinatorial bandit. By exploiting the combinatorial formulation,CombiSum manages to incorporate the notion of the extractive potential of each sentence of a document in its training. Finally, we propose LinCombiSum, the linear variant of Com-biSum which exploits the similarities between sentences in a document and uses the linear combinatorial bandit formulation instead
Document Type: Mémoire de maîtrise
Issue Date: 2021
Open Access Date: 28 June 2021
Grantor: Université Laval
Collection:Thèses et mémoires

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