  Understanding event descriptions is a central aspect of language processing,
but current approaches focus overwhelmingly on single sentences or documents.
Aggregating information about an event \emph{across documents} can offer a much
richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia
passages that \emph{report} on some event, paired with underlying,
genre-diverse (non-Wikipedia) \emph{source} articles for the same event. Events
and (cross-sentence) arguments in both report and source are annotated against
FrameNet, providing broad coverage of different event types. We present results
on two key event understanding tasks enabled by FAMuS: \emph{source validation}
-- determining whether a document is a valid source for a target report event
-- and \emph{cross-document argument extraction} -- full-document argument
extraction for a target event from both its report and the correct source
article. We release both FAMuS and our models to support further research.

Computation and Language