NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge

Anonymous

Paper Link: https://openreview.net/forum?id=EpXKbPSsYqL

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract:

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.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Alexander Spangher

Copyright Consent Name And Address: Information Sciences Institute, University of Southern California, 4676 Admiralty Way #1001, Marina Del Rey, CA 90292