@inproceedings{rennard-etal-2023-fredsum,
title = "{FREDS}um: A Dialogue Summarization Corpus for {F}rench Political Debates",
author = "Rennard, Virgile and
Shang, Guokan and
Grari, Damien and
Hunter, Julie and
Vazirgiannis, Michalis",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.280",
doi = "10.18653/v1/2023.findings-emnlp.280",
pages = "4241--4253",
abstract = "Recent advances in deep learning, and especially the invention of encoder-decoder architectures, have significantly improved the performance of abstractive summarization systems. While the majority of research has focused on written documents, we have observed an increasing interest in the summarization of dialogues and multi-party conversations over the past few years. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high-quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community, enabling further advances in multilingual dialogue summarization.",
}
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<abstract>Recent advances in deep learning, and especially the invention of encoder-decoder architectures, have significantly improved the performance of abstractive summarization systems. While the majority of research has focused on written documents, we have observed an increasing interest in the summarization of dialogues and multi-party conversations over the past few years. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high-quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community, enabling further advances in multilingual dialogue summarization.</abstract>
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%0 Conference Proceedings
%T FREDSum: A Dialogue Summarization Corpus for French Political Debates
%A Rennard, Virgile
%A Shang, Guokan
%A Grari, Damien
%A Hunter, Julie
%A Vazirgiannis, Michalis
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F rennard-etal-2023-fredsum
%X Recent advances in deep learning, and especially the invention of encoder-decoder architectures, have significantly improved the performance of abstractive summarization systems. While the majority of research has focused on written documents, we have observed an increasing interest in the summarization of dialogues and multi-party conversations over the past few years. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high-quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community, enabling further advances in multilingual dialogue summarization.
%R 10.18653/v1/2023.findings-emnlp.280
%U https://aclanthology.org/2023.findings-emnlp.280
%U https://doi.org/10.18653/v1/2023.findings-emnlp.280
%P 4241-4253
Markdown (Informal)
[FREDSum: A Dialogue Summarization Corpus for French Political Debates](https://aclanthology.org/2023.findings-emnlp.280) (Rennard et al., Findings 2023)
ACL