We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
@article{arxiv.1805.10399,
title = {Toward Abstractive Summarization Using Semantic Representations},
author = {Fei Liu and Jeffrey Flanigan and Sam Thomson and Norman Sadeh and Noah A. Smith},
journal= {arXiv preprint arXiv:1805.10399},
year = {2018}
}