  When summarizing a collection of views, arguments or opinions on some topic,
it is often desirable not only to extract the most salient points, but also to
quantify their prevalence. Work on multi-document summarization has
traditionally focused on creating textual summaries, which lack this
quantitative aspect. Recent work has proposed to summarize arguments by mapping
them to a small set of expert-generated key points, where the salience of each
key point corresponds to the number of its matching arguments. The current work
advances key point analysis in two important respects: first, we develop a
method for automatic extraction of key points, which enables fully automatic
analysis, and is shown to achieve performance comparable to a human expert.
Second, we demonstrate that the applicability of key point analysis goes well
beyond argumentation data. Using models trained on publicly available
argumentation datasets, we achieve promising results in two additional domains:
municipal surveys and user reviews. An additional contribution is an in-depth
evaluation of argument-to-key point matching models, where we substantially
outperform previous results.
