  During natural disasters and conflicts, information about what happened is
often confusing, messy, and distributed across many sources. We would like to
be able to automatically identify relevant information and assemble it into
coherent narratives of what happened. To make this task accessible to neural
models, we introduce Story Salads, mixtures of multiple documents that can be
generated at scale. By exploiting the Wikipedia hierarchy, we can generate
salads that exhibit challenging inference problems. Story salads give rise to a
novel, challenging clustering task, where the objective is to group sentences
from the same narratives. We demonstrate that simple bag-of-words similarity
clustering falls short on this task and that it is necessary to take into
account global context and coherence.
