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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 overdetermine 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 todoi:10.18653/v1/d18-1443 dblp:conf/emnlp/GehrmannDR18 fatcat:e4sptpdsozconn4u3li3ghcswq