FAMuS: Frames Across Multiple Sources
This addresses the challenge of aggregating event information across documents for richer language processing, though it is incremental as it builds on existing FrameNet annotations and focuses on specific tasks.
The authors tackled the problem of understanding events across multiple documents by introducing FAMuS, a new corpus of Wikipedia passages paired with diverse source articles, annotated with FrameNet for event and argument coverage. They achieved results on source validation and cross-document argument extraction tasks, releasing the corpus and models to support further research.
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.