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Recent work on abstractive summarization has made progress with neuralencoder-decoder architectures. However, such models are often challenged due totheir lack of explicit semantic modeling of the source document and itssummary. In this paper, we extend previous work on abstractive summarizationusing Abstract Meaning Representation (AMR) with a neural language generationstage which we guide using the source document. We demonstrate that thisguidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 usinggold standard AMR parses and parses obtained from an off-the-shelf parserrespectively. We also find that the summarization performance using the latteris 2 ROUGE-2 points higher than that of a well-established neuralencoder-decoder approach trained on a larger dataset. Code is available at\url{https://github.com/sheffieldnlp/AMR2Text-summ}

Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation