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.
%0 Journal Article
%1 Alshomary2021-ry
%A Alshomary, Milad
%A Gurcke, Timon
%A Syed, Shahbaz
%A Heinrich, Philipp
%A Spliethöver, Maximilian
%A Cimiano, Philipp
%A Potthast, Martin
%A Wachsmuth, Henning
%D 2021
%I arXiv
%K
%T Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
%X 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.
@article{Alshomary2021-ry,
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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinrich, Philipp and Splieth{\"o}ver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/240c51d85e38e54cbc58fdffadd195606/scadsfct},
interhash = {a6b46fb50fd5ed379ff411a890a3471e},
intrahash = {40c51d85e38e54cbc58fdffadd195606},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {Key Point Analysis via Contrastive Learning and Extractive Argument Summarization},
year = 2021
}
