Unsupervised Opinion Summarization

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

AmaSum is the largest abstractive opinion summarization dataset, consisting of more than 33,000 human-written summaries for Amazon products. Each summary is paired, on average, with more than 320 customer reviews. Summaries consist of verdicts, pros, and cons, see the example below.

SPACE is a large-scale opinion summarization benchmark for the evaluation of unsupervised summarizers. SPACE is built on TripAdvisor hotel reviews and includes a training set of approximately 1.1 million reviews for over 11 thousand hotels. For evaluation, we created a collection of human-written, abstractive opinion summaries for 50 hotels, including high-level general summaries and aspect summaries for six popular aspects: building, cleanliness, food, location, rooms, and service. Every summary is based on 100 input reviews, an order of magnitude increase compared to existing corpora. In total, SPACE contains 1,050 gold standard summaries. You can view the full instructions for out multi-stage annotation procedure here.