@inproceedings{xia-etal-2026-overview,
title = "Overview of the {LT}4{CPR} 2026 Shared Task: Generating Situation Reports from Social Media Messages",
author = "Xia, Fei and
Zhu, Haotian and
Lee, Yun Woo and
Grenev, Eitan and
Burton, Allison K and
Fan, Yingzhe and
Lewis, William D. and
Anastasopoulos, Antonios and
Ticona, Belu",
editor = "Ticona, Belu and
Xia, Fei and
Lewis, William D. and
Anastasopoulos, Antonios",
booktitle = "Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.lt4cpr-1.18/",
pages = "203--213",
ISBN = "979-8-89176-450-7",
abstract = "Language Technologies for Crisis Preparedness and Response (LT4CPR) encompass a broad range of natural language applications designed to support disaster response and preparedness. Within this domain, our shared task focuses specifically on leveraging social media data to extract actionable information and generate structured situation reports. To establish a formal benchmark, we introduce a domain-specific taxonomy for categorizing critical crisis information, alongside a novel pipeline for generating synthetic datasets to support task training and evaluation. Furthermore, we develop two baseline systems and propose an evaluation methodology designed to leverage the structural framework of situation reports. The shared task attracted participation from nine teams, whose diverse approaches offer valuable preliminary insights into the feasibility of structured situation report generation and the effectiveness of our evaluation setup. Finally, we highlight key open challenges (such as data access, operational alignment, noise filtering) which needs to be addressed in future research."
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<abstract>Language Technologies for Crisis Preparedness and Response (LT4CPR) encompass a broad range of natural language applications designed to support disaster response and preparedness. Within this domain, our shared task focuses specifically on leveraging social media data to extract actionable information and generate structured situation reports. To establish a formal benchmark, we introduce a domain-specific taxonomy for categorizing critical crisis information, alongside a novel pipeline for generating synthetic datasets to support task training and evaluation. Furthermore, we develop two baseline systems and propose an evaluation methodology designed to leverage the structural framework of situation reports. The shared task attracted participation from nine teams, whose diverse approaches offer valuable preliminary insights into the feasibility of structured situation report generation and the effectiveness of our evaluation setup. Finally, we highlight key open challenges (such as data access, operational alignment, noise filtering) which needs to be addressed in future research.</abstract>
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%0 Conference Proceedings
%T Overview of the LT4CPR 2026 Shared Task: Generating Situation Reports from Social Media Messages
%A Xia, Fei
%A Zhu, Haotian
%A Lee, Yun Woo
%A Grenev, Eitan
%A Burton, Allison K.
%A Fan, Yingzhe
%A Lewis, William D.
%A Anastasopoulos, Antonios
%A Ticona, Belu
%Y Ticona, Belu
%Y Xia, Fei
%Y Lewis, William D.
%Y Anastasopoulos, Antonios
%S Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-450-7
%F xia-etal-2026-overview
%X Language Technologies for Crisis Preparedness and Response (LT4CPR) encompass a broad range of natural language applications designed to support disaster response and preparedness. Within this domain, our shared task focuses specifically on leveraging social media data to extract actionable information and generate structured situation reports. To establish a formal benchmark, we introduce a domain-specific taxonomy for categorizing critical crisis information, alongside a novel pipeline for generating synthetic datasets to support task training and evaluation. Furthermore, we develop two baseline systems and propose an evaluation methodology designed to leverage the structural framework of situation reports. The shared task attracted participation from nine teams, whose diverse approaches offer valuable preliminary insights into the feasibility of structured situation report generation and the effectiveness of our evaluation setup. Finally, we highlight key open challenges (such as data access, operational alignment, noise filtering) which needs to be addressed in future research.
%U https://aclanthology.org/2026.lt4cpr-1.18/
%P 203-213
Markdown (Informal)
[Overview of the LT4CPR 2026 Shared Task: Generating Situation Reports from Social Media Messages](https://aclanthology.org/2026.lt4cpr-1.18/) (Xia et al., LT4CPR 2026)
ACL
- Fei Xia, Haotian Zhu, Yun Woo Lee, Eitan Grenev, Allison K Burton, Yingzhe Fan, William D. Lewis, Antonios Anastasopoulos, and Belu Ticona. 2026. Overview of the LT4CPR 2026 Shared Task: Generating Situation Reports from Social Media Messages. In Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response, pages 203–213, Hengqin, China. Association for Computational Linguistics.