Despite extensive research on parsing of English sentences into Abstraction
Meaning Representation (AMR) graphs, which are compared to gold graphs via the
Smatch metric, full-document parsing into a unified graph representation lacks
well-defined representation and evaluation. Taking advantage of a
super-sentential level of coreference annotation from previous work, we
introduce a simple algorithm for deriving a unified graph representation,
avoiding the pitfalls of information loss from over-merging and lack of
coherence from under-merging. Next, we describe improvements to the Smatch
metric to make it tractable for comparing document-level graphs, and use it to
re-evaluate the best published document-level AMR parser. We also present a
pipeline approach combining the top performing AMR parser and coreference
resolution systems, providing a strong baseline for future research.

本文介绍了一种简单的算法，可将超句级的共指标注转化为统一的图表达方式，避免了信息合并的信息损失和不协调的问题。通过改进 Smatch 度量方法，使其可以用于比较文档级别的图形，并用它来重新评估最佳发布的文档级 AMR 解析器，同时提供了一个将 AMR 解析器和共指消解系统相结合的管道方法，这为未来的研究提供了一个强大的基准。