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    <abstract>Structured crisis reports must preserve both information and its placement under canonical subsections. Merging reports generated from input chunks can omit subsections or duplicate their entries elsewhere. We introduce deterministic missing-subsection recovery and exact cross-title deduplication. The method reuses already generated model outputs, held fixed during postprocessing comparisons, without additional model calls. On 23 development cells, the combined postprocessor increased the official primary score from 0.4820 to 0.4890, a 1.45% relative gain over the baseline. Component comparisons attribute most of this development gain to recovery. On 117 test cells, all three submitted postprocessors scored 0.76–0.85% above the same baseline system on that split. Our primary system, GROUNDED, adds a fixed negative-template filter and placed fourth among nine teams; our highest-scoring submission also placed fourth in the best-system ranking. The results support structural correction of existing outputs, with small numerical differences between recovery policies across splits. They measure reference similarity, not factual entailment.</abstract>
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%0 Conference Proceedings
%T Frozen-Response Recovery for Evidence-Linked Crisis Situation Reports
%A Zheng, Quan
%A Wang, Ming
%A Tian, Yuanhe
%A Song, Yan
%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 zheng-etal-2026-frozen
%X Structured crisis reports must preserve both information and its placement under canonical subsections. Merging reports generated from input chunks can omit subsections or duplicate their entries elsewhere. We introduce deterministic missing-subsection recovery and exact cross-title deduplication. The method reuses already generated model outputs, held fixed during postprocessing comparisons, without additional model calls. On 23 development cells, the combined postprocessor increased the official primary score from 0.4820 to 0.4890, a 1.45% relative gain over the baseline. Component comparisons attribute most of this development gain to recovery. On 117 test cells, all three submitted postprocessors scored 0.76–0.85% above the same baseline system on that split. Our primary system, GROUNDED, adds a fixed negative-template filter and placed fourth among nine teams; our highest-scoring submission also placed fourth in the best-system ranking. The results support structural correction of existing outputs, with small numerical differences between recovery policies across splits. They measure reference similarity, not factual entailment.
%U https://aclanthology.org/2026.lt4cpr-1.17/
%P 188-202

Markdown (Informal)

[Frozen-Response Recovery for Evidence-Linked Crisis Situation Reports](https://aclanthology.org/2026.lt4cpr-1.17/) (Zheng et al., LT4CPR 2026)

• Frozen-Response Recovery for Evidence-Linked Crisis Situation Reports (Zheng et al., LT4CPR 2026)

ACL

• Quan Zheng, Ming Wang, Yuanhe Tian, and Yan Song. 2026. Frozen-Response Recovery for Evidence-Linked Crisis Situation Reports. In Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response, pages 188–202, Hengqin, China. Association for Computational Linguistics.