  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.
