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 mining.

翻译：论证是人类推理的基本要素，其中主张相互支持、质疑并权衡。我们提出了一种基于端到端大语言模型（LLM）的系统，用于从自然语言文本中重建抽象论证图。该系统采用多阶段流水线，逐步识别论证组成部分、选择相关元素并揭示其逻辑关系。这些元素被表示为有向无环图，包含两种成分类型（前提和结论）和三种关系类型（支持、攻击和削弱）。我们通过两个互补实验来评估该系统。首先，对取自论证理论教科书的论证进行人工评估，以检验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，通过将我们的输出映射到既定标注方案，实现与先前工作的比较。结果表明，该系统能充分恢复论证结构，并且在适应不同标注方案时，在基准数据集上取得了合理的性能。这些发现凸显了基于大语言模型的流水线在大规模论证挖掘中的潜力。