  Recent evaluation protocols for Cross-document (CD) coreference resolution
have often been inconsistent or lenient, leading to incomparable results across
works and overestimation of performance. To facilitate proper future research
on this task, our primary contribution is proposing a pragmatic evaluation
methodology which assumes access to only raw text -- rather than assuming gold
mentions, disregards singleton prediction, and addresses typical targeted
settings in CD coreference resolution. Aiming to set baseline results for
future research that would follow our evaluation methodology, we build the
first end-to-end model for this task. Our model adapts and extends recent
neural models for within-document coreference resolution to address the CD
coreference setting, which outperforms state-of-the-art results by a
significant margin.
