  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.

Computation and Language