From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
Arie Cattan, Lilach Eden, Yoav Kantor, Roy Bar-Haim

TL;DR
This paper introduces the task of organizing key points from textual comments into hierarchical structures to better understand related ideas at different levels of granularity, using a new dataset and improved prediction methods.
Contribution
It proposes the novel task of key point hierarchy construction, creates the ThinkP benchmark dataset, and develops advanced methods for predicting hierarchical relations among key points.
Findings
Significant performance improvements over baselines using directional distributional similarity.
Development of the ThinkP dataset for key point hierarchies in reviews.
Effective methods for inferring hierarchies from pairwise key point relations.
Abstract
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…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Sentiment Analysis and Opinion Mining · Topic Modeling
