Publication Details

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

authored by
Milad Alshomary, Timon Gurke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, Henning Wachsmuth

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.

External Organisation(s)
Paderborn University
Leipzig University
Bielefeld University
Conference contribution
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Language and Linguistics, Software, Linguistics and Language
Electronic version(s) (Access: Open)