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    <abstract>Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.</abstract>
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%0 Conference Proceedings
%T RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings
%A Zhou, Sitong
%A Yetisgen, Meliha
%A Ostendorf, Mari
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F zhou-etal-2026-radtimeline
%X Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.
%R 10.63317/32uhj8rx3u8f
%U https://aclanthology.org/2026.lrec-1.778/
%U https://doi.org/10.63317/32uhj8rx3u8f
%P 9920-9939

Markdown (Informal)

[RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings](https://aclanthology.org/2026.lrec-1.778/) (Zhou et al., LREC 2026)

• RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings (Zhou et al., LREC 2026)

ACL

• Sitong Zhou, Meliha Yetisgen, and Mari Ostendorf. 2026. RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9920–9939, Palma de Mallorca, Spain. ELRA Language Resource Association.