An analysis of document graph construction methods for AMR summarization
Abstract
Meaning Representation (AMR) is a graph-based semantic representation for sentences, composed of collections of concepts linked by semantic relations. AMR-based approaches have found success in a variety of applications, but a challenge to using it in tasks that require document-level context is that it only represents individual sentences. Prior work in AMR-based summarization has automatically merged the individual sentence graphs into a document graph, but the method of merging and its effects on summary content selection have not been independently evaluated. In this paper, we present a novel dataset consisting of human-annotated alignments between the nodes of paired documents and summaries which may be used to evaluate (1) merge strategies; and (2) the performance of content selection methods over nodes of a merged or unmerged AMR graph. We apply these two forms of evaluation to prior work as well as a new method for node merging and show that our new method has significantly better performance than prior work.
Cite
@article{arxiv.2111.13993,
title = {An analysis of document graph construction methods for AMR summarization},
author = {Fei-Tzin Lee and Chris Kedzie and Nakul Verma and Kathleen McKeown},
journal= {arXiv preprint arXiv:2111.13993},
year = {2021}
}