Textual knowledge bases such as Wikipedia require considerable effort to keep
up to date and consistent. While automated writing assistants could potentially
ease this burden, the problem of suggesting edits grounded in external
knowledge has been under-explored. In this paper, we introduce the novel
generation task of *faithfully reflecting updated information in text* (FRUIT)
where the goal is to update an existing article given new evidence. We release
the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data
produced from pairs of Wikipedia snapshots, along with our data generation
pipeline and a gold evaluation set of 914 instances whose edits are guaranteed
to be supported by the evidence. We provide benchmark results for popular
generation systems as well as EDIT5 -- a T5-based approach tailored to editing
we introduce that establishes the state of the art. Our analysis shows that
developing models that can update articles faithfully requires new capabilities
for neural generation models, and opens doors to many new applications.

本文介绍了一种新的生成任务 FRUIT，其目标是在给定新证据的情况下更新现有文章，为此我们发布了 FRUIT-WIKI 数据集并提供了基准结果和 EDIT5 等新方法。通过分析我们发现发展能够忠实地更新文章的模型需要新的神经生成模型能力，并且这种方法有许多新的应用领域。