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arXiv · 2005.01619

From Arguments to Key Points: Towards Automatic Argument Summarization

Roy Bar-Haim·Lilach Eden·Roni Friedman·Yoav Kantor·Dan Lahav·Noam Slonim
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Abstract

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.

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Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim. 2020-05-04. From Arguments to Key Points: Towards Automatic Argument Summarization. https://arxiv.org/abs/2005.01619

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