  Models for question answering, dialogue agents, and summarization often
interpret the meaning of a sentence in a rich context and use that meaning in a
new context. Taking excerpts of text can be problematic, as key pieces may not
be explicit in a local window. We isolate and define the problem of sentence
decontextualization: taking a sentence together with its context and rewriting
it to be interpretable out of context, while preserving its meaning. We
describe an annotation procedure, collect data on the Wikipedia corpus, and use
the data to train models to automatically decontextualize sentences. We present
preliminary studies that show the value of sentence decontextualization in a
user facing task, and as preprocessing for systems that perform document
understanding. We argue that decontextualization is an important subtask in
many downstream applications, and that the definitions and resources provided
can benefit tasks that operate on sentences that occur in a richer context.
