  Many real-world applications (e.g., note taking, search) require extracting a
sentence or paragraph from a document and showing that snippet to a human
outside of the source document. Yet, users may find snippets difficult to
understand as they lack context from the original document. In this work, we
use language models to rewrite snippets from scientific documents to be read on
their own. First, we define the requirements and challenges for this
user-facing decontextualization task, such as clarifying where edits occur and
handling references to other documents. Second, we propose a framework that
decomposes the task into three stages: question generation, question answering,
and rewriting. Using this framework, we collect gold decontextualizations from
experienced scientific article readers. We then conduct a range of experiments
across state-of-the-art commercial and open-source language models to identify
how to best provide missing-but-relevant information to models for our task.
Finally, we develop QaDecontext, a simple prompting strategy inspired by our
framework that improves over end-to-end prompting. We conclude with analysis
that finds, while rewriting is easy, question generation and answering remain
challenging for today's models.
