  Key point analysis is the task of extracting a set of concise and high-level
statements from a given collection of arguments, representing the gist of these
arguments. This paper presents our proposed approach to the Key Point Analysis
shared task, collocated with the 8th Workshop on Argument Mining. The approach
integrates two complementary components. One component employs contrastive
learning via a siamese neural network for matching arguments to key points; the
other is a graph-based extractive summarization model for generating key
points. In both automatic and manual evaluation, our approach was ranked best
among all submissions to the shared task.
