Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument. |
| Approach: | They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text. |
| Outcome: | The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available . |
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
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. |
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Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)
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| Challenge: | Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions . |
| Approach: | They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model . |
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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 . |
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Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
| Challenge: | In argumentation domain, people are exposed directly to audio (or the video), without access to a written version. |
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Detecting Attackable Sentences in Arguments (2020.emnlp-main)
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| Challenge: | Prior work in NLP studies focus on argument quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion. |
| Approach: | They propose to use machine learning to find attackable sentences in online arguments by analyzing driving reasons for attacks and identifying relevant characteristics of sentences. |
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Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments (2025.acl-long)
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| Challenge: | Identifying arguments is a prerequisite for various tasks in automated discourse analysis. |
| Approach: | They evaluate four BERT-like transformers on 17 English sentence-level datasets . they find that they tend to rely on lexical shortcuts tied to content words . |
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A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
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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. |
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