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.

本文提出了将大量讨论中的摘要表示为少量关键点的想法，并根据其显著性对每个关键点进行评分。通过分析大型数据集及实验结果表明，针对一个特定话题，少量关键点通常足以涵盖绝大部分的讨论内容，同时也发现领域专家通常可以提前预测这些关键点。文章探讨了将论点到关键点的映射任务，并引入了一个新的大型数据集，通过在该数据集上进行一系列实验，报告了有希望的表现。

从争论到关键点：自动争论摘要化