Related papers: CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization

CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization

URL: http://arxiv.org/abs/2210.14190v1
Date: Tue, 25 Oct 2022 17:32:40 GMT
Title: CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
Authors: Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, Alejandro Jaimes
Abstract summary: 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. Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks.
Score: 62.77066949111921
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.