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

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.

External Organisation(s)

Paderborn University
Leipzig University
Bielefeld University

Type

Conference contribution

Pages

184-189

No. of pages

6

Publication date

2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Language and Linguistics, Software, Linguistics and Language

Electronic version(s)

https://aclanthology.org/2021.argmining-1.19.pdf (Access: Open)