Bottom-up abstractive summarization


Gehrmann, S., Deng, Y. & Rush, A.M., 2018. Bottom-up abstractive summarization. EMNLP 2018.


Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but perform poorly 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  CNNDM 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.

Last updated on 11/25/2019