NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge
This work addresses the need for tools to analyze narrative and factual evolution in news articles, potentially aiding journalists and researchers, though it is incremental as it builds on existing edit analysis concepts.
The authors tackled the problem of analyzing news article revision histories by creating NewsEdits, the first publicly available large-scale dataset with 1.2 million articles and 4.6 million versions, and introduced three novel tasks for predicting edit actions, which were shown to be challenging for large NLP models.
News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021). We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.