@inproceedings{li-etal-2026-arg,
title = "Arg-{LL}a{DA}: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement",
author = "Li, Hao and
Sun, Yizheng and
Schlegel, Viktor and
Yang, Kailai and
Batista-Navarro, Riza and
Nenadic, Goran",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.336/",
pages = "7408--7421",
ISBN = "979-8-89176-390-6",
abstract = "Argument summarization aims to generate concise, structured representations of complex, multi-perspective debates. While recent work has advanced the identification and clustering of argumentative components, the generation stage remains underexplored. Existing approaches typically rely on single-pass generation, offering limited support for factual correction or structural refinement. To address this gap, we introduce Arg-LLaDA, a novel large language diffusion framework that iteratively improves summaries via sufficiency-guided remasking and regeneration. Our method combines a flexible masking controller with a sufficiency-checking module to identify and revise unsupported, redundant, or incomplete spans{---}yielding more faithful, concise, and coherent outputs. Empirical results on two benchmark datasets demonstrate that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 automatic evaluation metrics. In addition, human evaluations reveal substantial improvements across core dimensions, coverage, faithfulness, and conciseness,validating the effectiveness of our iterative, sufficiency-aware generation strategy."
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%0 Conference Proceedings
%T Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement
%A Li, Hao
%A Sun, Yizheng
%A Schlegel, Viktor
%A Yang, Kailai
%A Batista-Navarro, Riza
%A Nenadic, Goran
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-arg
%X Argument summarization aims to generate concise, structured representations of complex, multi-perspective debates. While recent work has advanced the identification and clustering of argumentative components, the generation stage remains underexplored. Existing approaches typically rely on single-pass generation, offering limited support for factual correction or structural refinement. To address this gap, we introduce Arg-LLaDA, a novel large language diffusion framework that iteratively improves summaries via sufficiency-guided remasking and regeneration. Our method combines a flexible masking controller with a sufficiency-checking module to identify and revise unsupported, redundant, or incomplete spans—yielding more faithful, concise, and coherent outputs. Empirical results on two benchmark datasets demonstrate that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 automatic evaluation metrics. In addition, human evaluations reveal substantial improvements across core dimensions, coverage, faithfulness, and conciseness,validating the effectiveness of our iterative, sufficiency-aware generation strategy.
%U https://aclanthology.org/2026.acl-long.336/
%P 7408-7421
Markdown (Informal)
[Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://aclanthology.org/2026.acl-long.336/) (Li et al., ACL 2026)
ACL