GeneFRDebate: Generated French Debates from News Articles with Industrial-Expert Summaries

Abstract

Summarizing domain-specific conversations, such as political debates, remains challenging despite advances in large language models (LLMs), and resources for French debates are particularly limited. We present GeneFRDebate, a new dataset of synthetic French political debates generated from real-world news articles using an LLM, while keeping expert-written summaries unchanged. Our pipeline combines prompt engineering, human curation, and quality evaluation using both automatic metrics and expert assessment. We also provide baseline experiments with small-scale LLMs (≤8B parameters), demonstrating the dataset’s usefulness for training and evaluation. This work shows that carefully generated synthetic data with human oversight can complement existing corpora, supporting research in multilingual and domain-specific dialogue summarization.

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