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    <abstract>No existing dataset adequately tests how well language models can incrementally update entity summaries – a crucial ability as these models rapidly advance. The Incremental Entity Summarization (IES) task is vital for maintaining accurate, up-to-date knowledge. To address this, we introduce , a fully synthetic dataset designed to expose real-world IES challenges. This dataset addresses issues like incorrect entity association and incomplete information, capturing real-world complexity by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy between generated summaries and paragraphs. Extensive experiments demonstrate the dataset‘s difficulty – state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. We will open-source the benchmark and the evaluation metrics to help the community make progress on IES tasks.</abstract>
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%0 Conference Proceedings
%T SUMIE: A Synthetic Benchmark for Incremental Entity Summarization
%A Hwang, Eunjeong
%A Zhou, Yichao
%A Gunel, Beliz
%A Wendt, James Bradley
%A Tata, Sandeep
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F hwang-etal-2025-sumie
%X No existing dataset adequately tests how well language models can incrementally update entity summaries – a crucial ability as these models rapidly advance. The Incremental Entity Summarization (IES) task is vital for maintaining accurate, up-to-date knowledge. To address this, we introduce , a fully synthetic dataset designed to expose real-world IES challenges. This dataset addresses issues like incorrect entity association and incomplete information, capturing real-world complexity by generating diverse attributes, summaries, and unstructured paragraphs with 99% alignment accuracy between generated summaries and paragraphs. Extensive experiments demonstrate the dataset‘s difficulty – state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. We will open-source the benchmark and the evaluation metrics to help the community make progress on IES tasks.
%U https://aclanthology.org/2025.coling-main.721/
%P 10839-10864

Markdown (Informal)

[SUMIE: A Synthetic Benchmark for Incremental Entity Summarization](https://aclanthology.org/2025.coling-main.721/) (Hwang et al., COLING 2025)

• SUMIE: A Synthetic Benchmark for Incremental Entity Summarization (Hwang et al., COLING 2025)

ACL

• Eunjeong Hwang, Yichao Zhou, Beliz Gunel, James Bradley Wendt, and Sandeep Tata. 2025. SUMIE: A Synthetic Benchmark for Incremental Entity Summarization. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10839–10864, Abu Dhabi, UAE. Association for Computational Linguistics.