# Automated Journalistic Questions: A New Method for Extracting 5W1H in French

arXiv: https://arxiv.org/abs/2505.14804 · HTML: https://mlsanity.com/paper/2505.14804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 3 of 3)

- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)

## Abstract (arXiv)

The 5W1H questions -- who, what, when, where, why and how -- are commonly used in journalism to ensure that an article describes events clearly and systematically. Answering them is a crucial prerequisites for tasks such as summarization, clustering, and news aggregation. In this paper, we design the first automated extraction pipeline to get 5W1H information from French news articles. To evaluate the performance of our algorithm, we also create a corpus of 250 Quebec news articles with 5W1H answers marked by four human annotators. Our results demonstrate that our pipeline performs as well in this task as the large language model GPT-4o.