  This work presents a new approach to unsupervised abstractive summarization
based on maximizing a combination of coverage and fluency for a given length
constraint. It introduces a novel method that encourages the inclusion of key
terms from the original document into the summary: key terms are masked out of
the original document and must be filled in by a coverage model using the
current generated summary. A novel unsupervised training procedure leverages
this coverage model along with a fluency model to generate and score summaries.
When tested on popular news summarization datasets, the method outperforms
previous unsupervised methods by more than 2 R-1 points, and approaches results
of competitive supervised methods. Our model attains higher levels of
abstraction with copied passages roughly two times shorter than prior work, and
learns to compress and merge sentences without supervision.
