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

Fuente: arXiv
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Autori principali: Verhaverbeke, Maxence, Gramaccia, Julie A., Khoury, Richard
Natura: Preprint
Pubblicazione: 2025
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author Verhaverbeke, Maxence
Gramaccia, Julie A.
Khoury, Richard
author_facet Verhaverbeke, Maxence
Gramaccia, Julie A.
Khoury, Richard
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_14804
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Automated Journalistic Questions: A New Method for Extracting 5W1H in French
Verhaverbeke, Maxence
Gramaccia, Julie A.
Khoury, Richard
Computation and Language
Machine Learning
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.
title Automated Journalistic Questions: A New Method for Extracting 5W1H in French
topic Computation and Language
Machine Learning
url https://arxiv.org/abs/2505.14804