  Existing approaches to automatic summarization assume that a length limit for
the summary is given, and view content selection as an optimization problem to
maximize informativeness and minimize redundancy within this budget. This
framework ignores the fact that human-written summaries have rich internal
structure which can be exploited to train a summarization system. We present
NEXTSUM, a novel approach to summarization based on a model that predicts the
next sentence to include in the summary using not only the source article, but
also the summary produced so far. We show that such a model successfully
captures summary-specific discourse moves, and leads to better content
selection performance, in addition to automatically predicting how long the
target summary should be. We perform experiments on the New York Times
Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model
summarization baselines by significant margins. We also show that the lengths
of summaries produced by our system correlates with the lengths of the
human-written gold standards.
