  The advent of large pre-trained language models has made it possible to make
high-quality predictions on how to add or change a sentence in a document.
However, the high branching factor inherent to text generation impedes the
ability of even the strongest language models to offer useful editing
suggestions at a more global or document level. We introduce a new task,
document sketching, which involves generating entire draft documents for the
writer to review and revise. These drafts are built from sets of documents that
overlap in form - sharing large segments of potentially reusable text - while
diverging in content. To support this task, we introduce a Wikipedia-based
dataset of analogous documents and investigate the application of weakly
supervised methods, including use of a transformer-based mixture of experts,
together with reinforcement learning. We report experiments using automated and
human evaluation methods and discuss relative merits of these models.
