Exploring Key Point Analysis with Pairwise Generation and Graph
  Partitioning
Xiao Li Yong Jiang Shen Huang Pengjun Xie Gong Cheng Fei Huang
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.