Challenge: ArgDiver model generates high-quality sentential arguments from multiple perspectives . retrieval-based systems do not have sufficient flexibility for input with missing keywords or topics unseen .
Approach: They propose a neural method to generate sentential arguments from multiple perspectives . their model generates high-quality sentential argument, but shows higher diversity .
Outcome: The proposed model generates high-quality sentential arguments from multiple perspectives . it shows that it can provide diverse perspectives on a controversial topic .

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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.
Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)

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Challenge: Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias.
Approach: They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation.
Outcome: The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset .
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.
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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.
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Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)

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Challenge: Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually.
Approach: They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures.
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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 .
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 .
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How Far Can We Extract Diverse Perspectives from Large Language Models? (2024.emnlp-main)

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Challenge: Recent advances of large language models have gained much interest from researchers to exploit their capability of creative generation for data augmentation with less cost and higher diversity.
Approach: They propose a criteria-based prompting technique to extract maximum diversity from LLMs.
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AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning (2022.emnlp-main)

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Challenge: Existing studies on argument generation focus on generating individual short arguments, while research on generating long and coherent argumentative essays is under-explored.
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Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)

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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.
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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.
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