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.

本文提出了目前为止最大的本地危机事件时间轴数据集 CrisisLTLSum，它包含了 1000 个危机事件时间轴数据，可用于基准测试时间轴拓展和抽象摘要技术的任务。该数据集是通过采用半自动化集群 - 精化方法从公共推特数据流中收集数据构建的，其初始实验表明，对于这两个任务而言，强基线与人类表现之间存在着显著差距，并且我们还公开了数据集、代码和模型。