Arguments are a fundamental aspect of human reasoning, in which claims are supported, challenged, and weighed against one another. We present an end-to-end large language model (LLM)-based system for reconstructing arguments from natural language text into abstract argument graphs. The system follows a multi-stage pipeline that progressively identifies argumentative components, selects relevant elements, and uncovers their logical relations. These elements are represented as directed acyclic graphs consisting of two component types (premises and conclusions) and three relation types (support, attack, and undercut). We conduct two complementary experiments to evaluate the system. First, we perform a manual evaluation on arguments drawn from an argumentation theory textbook to assess the system's ability to recover argumentative structure. Second, we conduct a quantitative evaluation on benchmark datasets, allowing comparison with prior work by mapping our outputs to established annotation schemes. Results show that the system can adequately recover argumentative structures and, when adapted to different annotation schemes, achieve reasonable performance across benchmark datasets. These findings highlight the potential of LLM-based pipelines for scalable argument reconstruction.

翻译：论证是人类推理的基本要素，其中主张被支持、挑战并相互权衡。我们提出一个基于大语言模型（LLM）的端到端系统，用于将自然语言文本中的论证重构为抽象论证图。该系统采用多阶段流水线，逐步识别论证组件、选择相关元素并揭示其逻辑关系。这些元素表示为有向无环图，包含两种组件类型（前提和结论）和三种关系类型（支持、攻击和削弱）。我们通过两项互补实验评估该系统：首先，对取自论证理论教科书的论证进行人工评估，以测试系统恢复论证结构的能力；其次，在基准数据集上进行定量评估，通过将输出映射到既定标注方案来与先前研究进行对比。结果表明，该系统能够有效恢复论证结构，并在适配不同标注方案后，在多个基准数据集上取得合理表现。这些发现凸显了基于LLM的流水线在可扩展论证重构方面的潜力。