FREDSum: A Dialogue Summarization Corpus for French Political Debates
Virgile Rennard Guokan Shang Damien Grari Julie Hunter Michalis Vazirgiannis
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.