Adapting Online Customer Reviews for Blind Users: A Case Study of Restaurant Reviews

TL;DR

This paper presents QuickQue, an assistive tool that reorganizes and summarizes online restaurant reviews for blind users, significantly improving usability and reducing listening fatigue.

Contribution

The paper introduces QuickQue, a novel aspect-focused summarization system using large language models to enhance review accessibility for blind users.

Findings

Significant usability improvements with QuickQue

Reduced listening fatigue for blind users

Effective review grouping and summarization

Abstract

Online reviews have become an integral aspect of consumer decision-making on e-commerce websites, especially in the restaurant industry. Unlike sighted users who can visually skim through the reviews, perusing reviews remains challenging for blind users, who rely on screen reader assistive technology that supports predominantly one-dimensional narration of content via keyboard shortcuts. In an interview study, we uncovered numerous pain points of blind screen reader users with online restaurant reviews, notably, the listening fatigue and frustration after going through only the first few reviews. To address these issues, we developed QuickQue assistive tool that performs aspect-focused sentiment-driven summarization to reorganize the information in the reviews into an alternative, thematically-organized presentation that is conveniently perusable with a screen reader. At its core, QuickQue utilizes a large language model to perform aspect-based joint classification for grouping reviews, followed by focused summarizations within the groups to generate concise representations of reviewers' opinions, which are then presented to the screen reader users via an accessible interface. Evaluation of QuickQue in a user study with 10 participants showed significant improvements in overall usability and task workload compared to the status quo screen reader.