mlsanityData to 14 Oct 2026

ArgCMV: An Argument Summarization Benchmark for the LLM-era

arXiv 2508.19580 ↗First posted 27 Aug 2025cs.CLcited by 3

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Abstract (arXiv)

Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extraction dataset called ArgCMV comprising of around 12K arguments from actual online human debates spread across over 3K topics. Our dataset exhibits higher complexity such as longer, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over ArgKP21. We show that existing methods do not adapt well to ArgCMV and provide extensive benchmark results by experimenting with existing baselines and latest open source models. This work introduces a novel KP extraction dataset for long-context online discussions, setting the stage for the next generation of LLM-driven summarization research.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 54 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

28 of 54 references.

References · 28

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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  4. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
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16 more
  1. 2020Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysiscited by 32
  2. 2024SLM-Mod: Small Language Models Surpass LLMs at Content Moderationcited by 27
  3. 2021Overview of the 2021 Key Point Analysis Shared Taskcited by 23
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  5. 2024Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMscited by 17
  6. 2021Key Point Analysis via Contrastive Learning and Extractive Argument Summarizationcited by 16
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  9. 2021Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matchingcited by 11
  10. 2023AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approachcited by 9
  11. 2024OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Datasetcited by 8
  12. 2023Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisationcited by 5
  13. 2024Exploring Key Point Analysis with Pairwise Generation and Graph Partitioningcited by 4
  14. 2025Argument Summarization and its Evaluation in the Era of Large Language Modelscited by 4
  15. 2024A Novel LLM-based Two-stage Summarization Approach for Long Dialoguescited by 3
  16. 2025Needling Through the Threads: A Visualization Tool for Navigating Threaded Online Discussionscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Argumentscited by 3
  2. 2026ArgBench: Benchmarking LLMs on Computational Argumentation Taskscited by 1
  3. 2026Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmarkcited by 0