Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

TL;DR

This paper introduces a novel approach for key point analysis that combines contrastive learning and extractive summarization, achieving top performance in a shared task on argument summarization.

Contribution

It presents a new method integrating contrastive learning and graph-based summarization for key point extraction from arguments, outperforming existing submissions.

Findings

Ranked best among all shared task submissions

Effective combination of contrastive learning and extractive summarization

Strong performance in both automatic and manual evaluations

Abstract

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.