  Recent work in neural generation has attracted significant interest in
controlling the form of text, such as style, persona, and politeness. However,
there has been less work on controlling neural text generation for content.
This paper introduces the notion of Content Transfer for long-form text
generation, where the task is to generate a next sentence in a document that
both fits its context and is grounded in a content-rich external textual source
such as a news story. Our experiments on Wikipedia data show significant
improvements against competitive baselines. As another contribution of this
paper, we release a benchmark dataset of 640k Wikipedia referenced sentences
paired with the source articles to encourage exploration of this new task.
