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Meaning Representation (AMR) is a graph-based semantic representation forsentences, composed of collections of concepts linked by semantic relations.AMR-based approaches have found success in a variety of applications, but achallenge to using it in tasks that require document-level context is that itonly represents individual sentences. Prior work in AMR-based summarization hasautomatically merged the individual sentence graphs into a document graph, butthe method of merging and its effects on summary content selection have notbeen independently evaluated. In this paper, we present a novel datasetconsisting of human-annotated alignments between the nodes of paired documentsand summaries which may be used to evaluate (1) merge strategies; and (2) theperformance of content selection methods over nodes of a merged or unmerged AMRgraph. We apply these two forms of evaluation to prior work as well as a newmethod for node merging and show that our new method has significantly betterperformance than prior work.

An analysis of document graph construction methods for AMR summarization