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:
this https URL

本文提出了一种新颖的抽象摘要框架，利用意义表示（AMR）的树库进行研究。在该框架中，源文本被解析为一组 AMR 图，该图被转换为摘要图，然后从摘要图生成文本。我们专注于图形到图形的转换，该转换将源语义图减少为摘要图，并利用现有的 AMR 解析器并假设最终可用 AMR-to-text 生成器。该框架具有数据驱动、可训练性和不特定于特定领域的特点。在金标准 AMR 注释和系统解析上进行的实验显示出有希望的结果。