Recent work within the Argument Mining community has shown the applicability of Natural Language Processing systems for solving problems found within competitive debate. One of the most important tasks within competitive debate is for debaters to create high quality debate cases. We show that effective debate cases can be constructed using constrained shortest path traversals on Argumentative Semantic Knowledge Graphs. We study this potential in the context of a type of American Competitive Debate, called Policy Debate, which already has a large scale dataset targeting it called DebateSum. We significantly improve upon DebateSum by introducing 53180 new examples, as well as further useful metadata for every example, to the dataset. We leverage the txtai semantic search and knowledge graph toolchain to produce and contribute 9 semantic knowledge graphs built on this dataset. We create a unique method for evaluating which knowledge graphs are better in the context of producing policy debate cases. A demo which automatically generates debate cases, along with all other code and the Knowledge Graphs, are open-sourced and made available to the public here: https://huggingface.co/spaces/Hellisotherpeople/DebateKG

翻译：最近论辩挖掘领域的研究表明，自然语言处理系统可有效解决竞技辩论中的问题。在竞技辩论中，最核心任务之一是让辩手构建高质量辩论案例。我们证明，通过约束最短路径遍历论辩性语义知识图谱可构建有效的辩论案例。本研究以美国竞技辩论中的"政策辩论"为背景展开——该领域已有名为DebateSum的大规模数据集。我们通过向该数据集新增53180个实例及每个实例的实用元数据，显著提升了DebateSum的性能。借助txtai语义搜索与知识图谱工具链，我们基于该数据集构建并贡献了9个语义知识图谱。我们独创了评估知识图谱在政策辩论案例生成场景中优劣的方法。演示系统（可自动生成辩论案例）、全部代码及知识图谱均已开源，可通过以下链接获取：https://huggingface.co/spaces/Hellisotherpeople/DebateKG