Recent advances in deep learning, and especially the invention of
encoder-decoder architectures, has significantly improved the performance of
abstractive summarization systems. The majority of research has focused on
written documents, however, neglecting the problem of multi-party dialogue
summarization. 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.

最近深度学习的进展，特别是编码器 - 解码器架构的发明，极大地改进了抽象摘要系统的性能。大部分研究集中在书面文件上，然而忽视了多方对话摘要的问题。本文提供了一组法国政治辩论的数据集，以增强多语对话摘要资源。我们的数据集包括手工转录和注释的政治辩论，涵盖一系列主题和观点。我们强调高质量的转录和注释对于训练准确有效的对话摘要模型的重要性，并强调支持非英语语言的对话摘要所需的多语资源。我们还使用最先进的方法提供了基准实验，并鼓励在这一领域进行进一步研究。我们的数据集将向研究社区公开提供。