| Challenge: | Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. |
| Approach: | They propose a controllable neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure. |
| Outcome: | The proposed model produces factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. |
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Aspect-Controlled Neural Argument Generation (2021.naacl-main)
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| Challenge: | Current argument generation models produce lengthy texts and allow the user little control over the aspect the argument should address. |
| Approach: | They propose a language model that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. |
| Outcome: | The proposed model generates high-quality arguments for argumentation and counter-arguments. |
Neural Argument Generation Augmented with Externally Retrieved Evidence (P18-1)
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| Challenge: | Existing methods for generating arguments are limited to retrieval-based methods. |
| Approach: | They propose an encoder-decoder-based argument generation model enriched with externally retrieved evidence from Wikipedia. |
| Outcome: | The proposed model generates arguments with more topic-relevant content than current models based on automatic evaluation and human assessments on a large-scale dataset from reddit. |
Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021.acl-long)
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| Challenge: | Existing methods for generating arguments use end-to-end knowledge graphs or are controlled with respect to the argument's topic, aspects, or stance. |
| Approach: | They construct and populate three knowledge graphs and encode them into debate portals and relevant paragraphs from Wikipedia. |
| Outcome: | The proposed model produces arguments with superior quality than those generated without knowledge. |
ArgGenBench: Benchmarking the Complex Controlled Argument Generation Capability of Large Language Models (2026.acl-long)
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| Challenge: | Existing studies focus on limited control signals such as topic, stance, length, style, strategy, audience, and key aspects, failing to capture this complexity. |
| Approach: | They propose a benchmark that integrates multi-dimensional control into a single instruction to evaluate LLMs' ability to produce persuasive arguments. |
| Outcome: | The proposed benchmarks show that existing models fail to capture multifaceted argumentative control signals. |
Belief-based Generation of Argumentative Claims (2021.eacl-main)
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| Challenge: | Existing methods to generate argument with the ability to encode beliefs are limited by the noise generated by the automatic collection of bag-of-words. |
| Approach: | They propose to augment argument generation technology with ability to encode beliefs . they model users' beliefs via their stances on big issues and extend text generation models with extra input reflecting user's beliefs. |
| Outcome: | The proposed approach is low in effectiveness because of the noise produced by the automatic collection of bag-of-words. |
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)
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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges. |
| Approach: | They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments . |
| Outcome: | The proposed framework and evaluator are competitive in counter-argument generation tasks. |
Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
| Approach: | They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia . |
| Outcome: | The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models. |
Conclusion-based Counter-Argument Generation (2023.eacl-main)
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| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
| Outcome: | The proposed approach generates more relevant and stance-adhering counters than strong baselines. |
TARGER: Neural Argument Mining at Your Fingertips (P19-3)
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Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
| Approach: | They propose to use TARGER to tagging arguments in free text and keyword-based retrieval of arguments from a web-scale corpus. |
| Outcome: | The proposed framework can be used without any reproducibility effort on the user's side and is easily portable to other domains and use cases. |
Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement (2026.acl-long)
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| Challenge: | Existing approaches to argument summarization rely on single-pass generation, offering limited support for factual correction or structural refinement. |
| Approach: | They propose a large language diffusion framework that iteratively improves argument summarization by sufficiency-guided remasking and regeneration. |
| Outcome: | Empirical results show that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 evaluation metrics. |