  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.
