Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

verfasst von

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.

Externe Organisation(en)

Universität Paderborn
Universität Leipzig
Universität Bielefeld

Typ

Aufsatz in Konferenzband

Seiten

184-189

Anzahl der Seiten

6

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Sprache und Linguistik, Software, Linguistik und Sprache

Elektronische Version(en)

https://aclanthology.org/2021.argmining-1.19.pdf (Zugang: Offen)