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: this https URL

使用语义知识图谱，通过在竞争性辩论中构建高质量辩论案例的有约束的最短路径遍历，有效地改进了辩论案例生成，并且为 Policy Debate 提供了大规模数据集以及有用的元数据。