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    <abstract>Argument summarization requires capturing semantic content while preserving the underlying argumentative structure. We present ArgSum-X, a dataset supporting both argumentative text summarization (ATS) and key point analysis (KPA) . To construct the dataset, we propose constructed via a multi-stage annotation framework with hybrid LLM–human annotation and quality tiers, and provide a detailed analysis of annotation quality. We propose a comprehensive evaluation framework for argument summarization covering structural, faithfulness, and semantic aspects, and conduct an empirical analysis of structure-aware modeling, revealing the unique properties and challenges of argument summarization.</abstract>
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%0 Conference Proceedings
%T ArgSum-X: A Structure-Aware Benchmark for Argument Summarization and Key Point Analysis
%A Shi, Zhiqiang
%A Cocarascu, Oana
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F shi-cocarascu-2026-argsum
%X Argument summarization requires capturing semantic content while preserving the underlying argumentative structure. We present ArgSum-X, a dataset supporting both argumentative text summarization (ATS) and key point analysis (KPA) . To construct the dataset, we propose constructed via a multi-stage annotation framework with hybrid LLM–human annotation and quality tiers, and provide a detailed analysis of annotation quality. We propose a comprehensive evaluation framework for argument summarization covering structural, faithfulness, and semantic aspects, and conduct an empirical analysis of structure-aware modeling, revealing the unique properties and challenges of argument summarization.
%U https://aclanthology.org/2026.emnlp-main.403/
%P 9263-9309

Markdown (Informal)

[ArgSum-X: A Structure-Aware Benchmark for Argument Summarization and Key Point Analysis](https://aclanthology.org/2026.emnlp-main.403/) (Shi & Cocarascu, EMNLP 2026)

• ArgSum-X: A Structure-Aware Benchmark for Argument Summarization and Key Point Analysis (Shi & Cocarascu, EMNLP 2026)

ACL

• Zhiqiang Shi and Oana Cocarascu. 2026. ArgSum-X: A Structure-Aware Benchmark for Argument Summarization and Key Point Analysis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9263–9309, Budapest, Hungary. Association for Computational Linguistics.