Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed "key points", each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.
@article{arxiv.2005.01619,
title = {From Arguments to Key Points: Towards Automatic Argument Summarization},
author = {Roy Bar-Haim and Lilach Eden and Roni Friedman and Yoav Kantor and Dan Lahav and Noam Slonim},
journal= {arXiv preprint arXiv:2005.01619},
year = {2020}
}