arxiv_cl 95% Match Research Paper NLP Researchers,AI Developers,Argument Mining Specialists,Legal Tech Professionals 3 weeks ago

Argument Summarization and its Evaluation in the Era of Large Language Models

large-language-models › evaluation

📄 Abstract

Abstract: Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining. This paper investigates the integration of state-of-the-art LLMs into ArgSum systems and their evaluation. In particular, we propose a novel prompt-based evaluation scheme, and validate it through a novel human benchmark dataset. Our work makes three main contributions: (i) the integration of LLMs into existing ArgSum systems, (ii) the development of two new LLM-based ArgSum systems, benchmarked against prior methods, and (iii) the introduction of an advanced LLM-based evaluation scheme. We demonstrate that the use of LLMs substantially improves both the generation and evaluation of argument summaries, achieving state-of-the-art results and advancing the field of ArgSum. We also show that among the four LLMs integrated in (i) and (ii), Qwen-3-32B, despite having the fewest parameters, performs best, even surpassing GPT-4o.

Key Contributions

Investigates the integration of LLMs into Argument Summarization (ArgSum) systems and proposes a novel prompt-based evaluation scheme validated by a new human benchmark dataset. The paper develops two new LLM-based ArgSum systems, demonstrating substantial improvements in both generation and evaluation, and identifies Qwen-3-32B as a top performer.

Business Value

Enhances the ability to automatically generate high-quality summaries of arguments, useful for legal analysis, policy making, debate preparation, and understanding complex discussions, making information more accessible and digestible.

Paper Metadata

Innovation Type

Methodological Improvement and System Development

Deployment Feasibility

High, as it builds upon existing LLM capabilities and standard NLG evaluation practices.

Limitations Addressed

Challenges in evaluating Argument Summarization and effectively integrating LLMs into this specific NLG task.

Performance Gains

Substantially improves both the generation and evaluation of argument summaries, achieving state-of-the-art results.

Technical Tags

Argument Summarization (ArgSum)Large Language Models (LLMs)Natural Language Generation (NLG)Prompt-based evaluationHuman benchmark datasetLLM-based ArgSum systemsQwen-3-32BArgument Mining

Research Topics

Argument MiningText SummarizationLLM ApplicationsEvaluation MethodologiesNatural Language Generation

Methods & Architectures

Integration of LLMs into ArgSum systemsDevelopment of new LLM-based ArgSum systemsPrompt-based evaluation schemeHuman benchmark dataset creation TransformerLarge Language Models (LLMs)

Applications & Tasks

Natural Language Processing Argument Mining Text Summarization Evaluating Argument SummarizationIntegrating LLMs into NLG tasksDeveloping robust evaluation schemes Argument SummarizationLLM EvaluationNatural Language Generation

Datasets & Benchmarks

Datasets

novel human benchmark dataset

Benchmarks

State-of-the-art results in ArgSum generation and evaluation.

Quality of argument summariesEffectiveness of prompt-based evaluation

Related Fields

Natural Language ProcessingArgument MiningText SummarizationLarge Language ModelsMachine Learning

Keywords

Argument SummarizationLLMNLGevaluationpromptingArgument MiningQwenbenchmarksummarizationNLP

Academic Context

#Argument Mining#Text Summarization#LLM Applications#Evaluation Methodologies#Natural Language Generation

Commercial Potential

Potential Products

Automated argument summarization toolsAI-powered legal research assistantsTools for analyzing public discourse

Target Industries

LegalGovernmentMediaAcademiaTechnology

Use Case Examples

Summarizing legal arguments from court documents.Generating concise summaries of parliamentary debates.Analyzing customer feedback for key arguments.

Competitive Edge

Advances the state-of-the-art in argument summarization by leveraging LLMs and introducing a more robust evaluation framework.

Market Opportunity

Significant, as argument analysis is relevant across many sectors.

Revenue Models

Could be offered as a service or integrated into existing NLP platforms.

Resource Requirements

Compute Needs

Moderate to high, depending on the LLM used for generation and evaluation.

Data Requirements

Requires a dataset of arguments and their summaries, plus a human benchmark dataset for evaluation.

Deployment Constraints

Prompt engineering for evaluation can be complex.

Scalability

Scalability depends on the LLM used; the evaluation framework itself is scalable.

Production Readiness

Maturity Level

Research

Time to Market

Medium, requires integration and validation.

Patent Potential

Moderate, for the novel prompt-based evaluation scheme.

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