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

Alexander Spangher, Xiang Ren, Jonathan May, Nanyun Peng
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引用次数: 12

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.
新闻编辑:一个新闻文章修订数据集和一个新的文档级推理挑战
新闻文章修改史为新闻文章的叙事和事实演变提供了线索。为了便于分析这一演变,我们提出了第一个公开可用的新闻修订历史数据集NewsEdits。我们的数据集是大规模和多语言的;它包含120万篇文章,460万个版本,来自三个国家的22种英语和法语报纸,涵盖15年(2006-2021)。我们定义了文章级别的编辑操作:添加、删除、编辑和重构,并开发了一个高精度的提取算法来识别这些操作。为了强调许多编辑行为的事实性,我们进行了分析,表明添加和删除的句子比未修改的句子更有可能包含更新事件、主要内容和引用。最后,为了探索编辑操作是否可预测,我们引入了三个旨在预测版本更新期间执行的操作的新任务。我们表明,这些任务对于专家来说是可能的,但对于大型NLP模型来说是具有挑战性的。我们希望这能刺激对叙事框架的研究,并为追逐突发新闻的记者提供预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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