  Neural network-based methods for abstractive summarization produce outputs
that are more fluent than other techniques, but which can be poor at content
selection. This work proposes a simple technique for addressing this issue: use
a data-efficient content selector to over-determine phrases in a source
document that should be part of the summary. We use this selector as a
bottom-up attention step to constrain the model to likely phrases. We show that
this approach improves the ability to compress text, while still generating
fluent summaries. This two-step process is both simpler and higher performing
than other end-to-end content selection models, leading to significant
improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the
content selector can be trained with as little as 1,000 sentences, making it
easy to transfer a trained summarizer to a new domain.
