Menu principale


Acceuil
Publications
Auteur princpal :
Nouha Dammak







Titre :
Abstractive meeting speech summarization based on an attentional model
Conférence :
Thirteenth International Conference on Machine Vision
Mois :
novembre
Année :
2020
Journal, revue, proceedings ... :
Pays :
Italie
Ville :
Rome
Téléchargements :
Type de publication :
Conférence intertnationale
Abstract :
Through the ages, in all nations, at all times, people spend a lot of their time on discussing new and important issues
either on meetings or in conferences. With the evolution and the abundance of Automatic Speech Recognition (ASR)
frameworks, automatic transcripts and even automatic meeting summarization are getting more and more interesting.
Recently, automatic summarization faces deeper progresses on speech summarization. Neural models had been
introduced to tackle with many difficulties of abstractive summarization. Our contribution in this paper focuses on these
weaknesses of neural abstractive meeting summarization and suggests an encoder-decoder model based on an attentional
algorithm on the decoding sequence. We proposed a deep encoder-decoder model based on attention mechanism
(DEDA) for ASR transcripts. Experiments on the AMI Dataset demonstrates that our proposed method ensured
competitive results with the state of the art even on extractive or abstractive models. The experimental analyses also put
the stress on the performance of the summarized utterances as well as the reduction of the occurrence repetition in
summaries.