  Cross-document event coreference resolution (CDCR) is an NLP task in which
mentions of events need to be identified and clustered throughout a collection
of documents. CDCR aims to benefit downstream multi-document applications, but
despite recent progress on corpora and system development, downstream
improvements from applying CDCR have not been shown yet. We make the
observation that every CDCR system to date was developed, trained, and tested
only on a single respective corpus. This raises strong concerns on their
generalizability -- a must-have for downstream applications where the magnitude
of domains or event mentions is likely to exceed those found in a curated
corpus. To investigate this assumption, we define a uniform evaluation setup
involving three CDCR corpora: ECB+, the Gun Violence Corpus and the Football
Coreference Corpus (which we reannotate on token level to make our analysis
possible). We compare a corpus-independent, feature-based system against a
recent neural system developed for ECB+. Whilst being inferior in absolute
numbers, the feature-based system shows more consistent performance across all
corpora whereas the neural system is hit-and-miss. Via model introspection, we
find that the importance of event actions, event time, etc. for resolving
coreference in practice varies greatly between the corpora. Additional analysis
shows that several systems overfit on the structure of the ECB+ corpus. We
conclude with recommendations on how to achieve generally applicable CDCR
systems in the future -- the most important being that evaluation on multiple
CDCR corpora is strongly necessary. To facilitate future research, we release
our dataset, annotation guidelines, and system implementation to the public.
