Unified Key Point Matching

Key Point Matching (KPM) aims to identify and match the most relevant key points from a set of arguments related to a specific topic. While traditional multi-document summarization techniques focus on extractive or abstractive summarization, this study proposes a novel approach, Matching The Statements (MTS), which leverages advanced pre-trained language models and incorporates topic information for im- provedkeypointanalysis.Ourmethodutilizesaunifiedmodeltointegrate contextual information, enhancing semantic similarity evaluation between arguments and key points. Through extensive experimentation on the ArgKP-2021 dataset, MTS demonstrates significant performance improve- ments over baseline methods. This paper outlines the architecture of MTS, details the data preparation and encoding processes, and presents results validating the model’s efficacy in the KPM task.