@inproceedings{rajaby-faghihi-etal-2022-crisisltlsum,
title = "{C}risis{LTLS}um: A Benchmark for Local Crisis Event Timeline Extraction and Summarization",
author = "Rajaby Faghihi, Hossein and
Alhafni, Bashar and
Zhang, Ke and
Ran, Shihao and
Tetreault, Joel and
Jaimes, Alejandro",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.400",
doi = "10.18653/v1/2022.findings-emnlp.400",
pages = "5455--5477",
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 , the largest dataset of local crisis event timelines available to date. contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built 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 (\url{https://github.com/CrisisLTLSum/CrisisTimelines}).",
}
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<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 , the largest dataset of local crisis event timelines available to date. contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built 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 (https://github.com/CrisisLTLSum/CrisisTimelines).</abstract>
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%0 Conference Proceedings
%T CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
%A Rajaby Faghihi, Hossein
%A Alhafni, Bashar
%A Zhang, Ke
%A Ran, Shihao
%A Tetreault, Joel
%A Jaimes, Alejandro
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F rajaby-faghihi-etal-2022-crisisltlsum
%X 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 , the largest dataset of local crisis event timelines available to date. contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built 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 (https://github.com/CrisisLTLSum/CrisisTimelines).
%R 10.18653/v1/2022.findings-emnlp.400
%U https://aclanthology.org/2022.findings-emnlp.400
%U https://doi.org/10.18653/v1/2022.findings-emnlp.400
%P 5455-5477
Markdown (Informal)
[CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization](https://aclanthology.org/2022.findings-emnlp.400) (Rajaby Faghihi et al., Findings 2022)
ACL