Every Bite Is an Experience: Key Point Analysis of Business Reviews

TL;DR

This paper enhances Key Point Analysis (KPA) for business reviews by integrating sentiment analysis, improving key point extraction, and leveraging review metadata, resulting in more comprehensive and explainable summaries without requiring domain-specific annotations.

Contribution

The paper introduces novel extensions to KPA, including Collective Key Point Mining and sentiment integration, to improve review summarization and explanation.

Findings

Enhanced KPA performance with new extensions

Effective review summarization without domain-specific data

Human supervision further improves results

Abstract

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.