论文标题
从参数到要点：朝向自动参数摘要
From Arguments to Key Points: Towards Automatic Argument Summarization
论文作者
论文摘要
从有关给定主题的大量论点中产生简明的摘要是一个有趣而又研究的问题。我们建议将这样的摘要表示为一小部分谈话要点，称为“关键点”，每个都根据其显着性得分。我们通过分析大量的人群分配参数的数据集，表明每个主题的少数要点通常足以涵盖绝大多数参数。此外，我们发现域专家通常可以提前预测这些关键点。我们研究了争论点映射的任务，并为此任务介绍了一个新颖的大规模数据集。我们报告了该数据集的一系列广泛实验的经验结果，显示出令人鼓舞的性能。
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.
