  Social media has increasingly played a key role in emergency response: first
responders can use public posts to better react to ongoing crisis events and
deploy the necessary resources where they are most needed. Timeline extraction
and abstractive summarization are critical technical tasks to leverage large
numbers of social media posts about events. Unfortunately, there are few
datasets for benchmarking technical approaches for those tasks. This paper
presents CrisisLTLSum, the largest dataset of local crisis event timelines
available to date. CrisisLTLSum contains 1,000 crisis event timelines across
four domains: wildfires, local fires, traffic, and storms. We built
CrisisLTLSum using a semi-automated cluster-then-refine approach to collect
data from the public Twitter stream. Our initial experiments indicate a
significant gap between the performance of strong baselines compared to the
human performance on both tasks. Our dataset, code, and models are publicly
available.
