CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, Alejandro Jaimes
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).- Anthology ID:
- 2022.findings-emnlp.400
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5455–5477
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.findings-emnlp.400/
- DOI:
- 10.18653/v1/2022.findings-emnlp.400
- Cite (ACL):
- Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, and Alejandro Jaimes. 2022. CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5455–5477, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization (Rajaby Faghihi et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.findings-emnlp.400.pdf