Key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets.

翻译：关键点分析（Key Point Analysis, KPA）旨在将多个论点归纳为简洁的关键点集合，且至今仍是论据挖掘领域内一个显著且未解决的难题。现有模型采用两阶段流水线，即对论点进行聚类或为论点聚类生成关键点。这种方法依赖于语义相似性，而非衡量论点间是否存在共享关键点。此外，它仅建模了论点间的簇内关系，忽略了不共享关键点的论点间的簇间关系。为解决这些局限，我们提出了一种结合配对生成与图分割的新颖KPA方法。目标是训练一个生成模型，使其能同时提供一对论点间是否存在共享关键点的评分，并生成该共享关键点。随后，为将生成的多余关键点映射到一组简洁的关键点，我们构建一个论点图：以论点为顶点，生成的关键点为边，评分作为边权重。接着，我们提出一种图分割算法，将所有共享相同关键点的论点划分至同一子图。值得注意的是，实验结果表明，在ArgKP和QAM数据集上评估时，我们所提出的模型性能超越了以往的模型。