Ms. Nouha Dammak Profile

Nouha Dammak

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Proceedings Article | 4 January 2021 Paper

Abstractive meeting summarization based on an attentional neural model

Nouha Dammak, Yassine BenAyed

Proceedings Volume 11605, 1160504 (2021) https://doi.org/10.1117/12.2587172

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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.

Proceedings Article | 13 April 2018 Paper

Hierarchical vs non-hierarchical audio indexation and classification for video genres

Nouha Dammak, Yassine BenAyed

Proceedings Volume 10696, 1069621 (2018) https://doi.org/10.1117/12.2309852

KEYWORDS: Video, Feature extraction, Visualization, Databases, Information visualization, Fourier transforms, Classification systems

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In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub-classification may divide each genre into several multi-speaker and multi-dialect sub-genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%.

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