  Storytelling, whether via fables, news reports, documentaries, or memoirs,
can be thought of as the communication of interesting and related events that,
taken together, form a concrete process. It is desirable to extract the event
chains that represent such processes. However, this extraction remains a
challenging problem. We posit that this is due to the nature of the texts from
which chains are discovered. Natural language text interleaves a narrative of
concrete, salient events with background information, contextualization,
opinion, and other elements that are important for a variety of necessary
discourse and pragmatics acts but are not part of the principal chain of events
being communicated. We introduce methods for extracting this principal chain
from natural language text, by filtering away non-salient events and supportive
sentences. We demonstrate the effectiveness of our methods at isolating
critical event chains by comparing their effect on downstream tasks. We show
that by pre-training large language models on our extracted chains, we obtain
improvements in two tasks that benefit from a clear understanding of event
chains: narrative prediction and event-based temporal question answering. The
demonstrated improvements and ablative studies confirm that our extraction
method isolates critical event chains.
