Bottom-Up Abstractive Summarization

EMNLP 2018 Sebastian GehrmannYuntian DengAlexander M. Rush

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... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Document Summarization CNN / Daily Mail Bottom-Up Sum PPL 32.75 # 2
Document Summarization CNN / Daily Mail Bottom-Up Sum ROUGE-1 41.22 # 6
Document Summarization CNN / Daily Mail Bottom-Up Sum ROUGE-2 18.68 # 6
Document Summarization CNN / Daily Mail Bottom-Up Sum ROUGE-L 38.34 # 6