  Recent work on abstractive summarization has made progress with neural
encoder-decoder architectures. However, such models are often challenged due to
their lack of explicit semantic modeling of the source document and its
summary. In this paper, we extend previous work on abstractive summarization
using Abstract Meaning Representation (AMR) with a neural language generation
stage which we guide using the source document. We demonstrate that this
guidance improves summarization results 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. We also find that the summarization performance using the latter
is 2 ROUGE-2 points higher than that of a well-established neural
encoder-decoder approach trained on a larger dataset. Code is available at
\url{https://github.com/sheffieldnlp/AMR2Text-summ}
