  Key Point Analysis (KPA) has been recently proposed for deriving fine-grained
insights from collections of textual comments. KPA extracts the main points in
the data as a list of concise sentences or phrases, termed key points, and
quantifies their prevalence. While key points are more expressive than word
clouds and key phrases, making sense of a long, flat list of key points, which
often express related ideas in varying levels of granularity, may still be
challenging. To address this limitation of KPA, we introduce the task of
organizing a given set of key points into a hierarchy, according to their
specificity. Such hierarchies may be viewed as a novel type of Textual
Entailment Graph. We develop ThinkP, a high quality benchmark dataset of key
point hierarchies for business and product reviews, obtained by consolidating
multiple annotations. We compare different methods for predicting pairwise
relations between key points, and for inferring a hierarchy from these pairwise
predictions. In particular, for the task of computing pairwise key point
relations, we achieve significant gains over existing strong baselines by
applying directional distributional similarity methods to a novel
distributional representation of key points, and further boost performance via
weak supervision.
