Papers with neural language generation stage

1 papers
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)

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Challenge: Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary.
Approach: They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document.
Outcome: The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively.

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