FREDSum：一个用于法国政治辩论的对话摘要语料库
FREDSum: A Dialogue Summarization Corpus for French Political Debates
AI总结：
本文构建了人工转录与标注的法国政治辩论数据集 FREDSum，并提供基于最先进方法的基线实验，以弥补多语言、多方对话摘要资源的不足。
摘要：
深度学习的最新进展，尤其是编码器-解码器架构的发明，显著提升了抽象式摘要系统的性能。然而，大多数研究都集中在书面文档上，忽视了多方对话摘要的问题。在本文中，我们提出了一个法国政治辩论数据集，旨在丰富多语言对话摘要资源。我们的数据集由人工转录和标注的政治辩论组成，涵盖一系列主题和观点。我们强调了高质量转录和标注对于训练准确、有效的对话摘要模型的重要性，并强调需要多语言资源以支持非英语语言的对话摘要。我们还使用最先进的方法提供了基线实验，并鼓励在该领域开展进一步研究，以推动对话摘要领域的发展。我们的数据集将公开发布，供研究界使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
