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Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

2021-09-30 12:54:26
Milad Alshomary, Timon Gurke, Shahbaz Syed, Philipp Heinrich, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, Henning Wachsmuth
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2109.15086

PDF

https://arxiv.org/pdf/2109.15086.pdf

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