Rationale-based Opinion Summarization: Extracting Representative Opinions and Informative Rationales from Online Reviews

Core Concepts

Rationale-based opinion summarization generates concise summaries that present representative opinions along with corresponding rationales, providing more informative and useful summaries compared to conventional opinion summaries.

Abstract

The paper proposes a new paradigm for summarizing online reviews, called rationale-based opinion summarization. Conventional opinion summaries often lack supporting details and can be too generic. To address this, the authors introduce rationale-based opinion summaries, which output representative opinions as well as one or more corresponding rationales (supporting details).
The authors define four desirable properties of good rationales: relatedness, specificity, popularity, and diversity. They present an unsupervised extractive system called RATION that has two components: an Opinion Extractor to extract representative opinions, and a Rationales Extractor to extract corresponding rationales.
The Opinion Extractor uses an existing extractive summarization model to identify summarizing review sentences, and then extracts representative opinions of the form "A is B" from these sentences. To remove redundancy, it uses a graph-based approach to cluster similar representative opinions.
The Rationales Extractor first estimates the four desirable properties of rationales using an alignment model fine-tuned on in-domain data. It then uses Gibbs sampling to extract a user-specified number of rationales for each representative opinion, approximating the joint probability of the rationales based on these properties.
Human evaluation shows that rationale-based summaries generated by RATION are less redundant, more coherent, and more useful for decision-making compared to conventional opinion summaries. Automatic evaluation also demonstrates that the rationales extracted by RATION have better quality than strong baselines.

Stats

The rooms are very clean and spacious.
The staff was friendly and helpful.
The hotel was in a great location, fabulous views and fantastic service.
The rooms were clean and the pool was nice.
The rooms are large and I had a great view of the bay.
The room was clean and the rates were very reasonable.

Quotes

"the staff (Sylvia) and the entire front desk staff were very professional, efficient and always helpful."
"you can't beat the location for walking access to Coconut Grove boutiques, restaurants, movies and even the Post Office."
"great views from every room & a nice balcony."

Key Insights Distilled From

Rationale-based Opinion Summarization

by Haoyuan Li,S... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00217.pdf

Deeper Inquiries

How can the rationale-based summarization approach be extended to other domains beyond online reviews, such as scientific papers or news articles

Rationale-based summarization can be extended to other domains by adapting the approach to the specific characteristics of the domain. For scientific papers, the system could focus on extracting key findings, methodologies, and contributions as representative opinions, with rationales being supporting evidence or experimental results. In news articles, the system could identify main points, arguments, or perspectives as representative opinions, with rationales being quotes, statistics, or expert opinions that support those points. By customizing the criteria for good rationales based on the domain, the system can effectively summarize content from various sources beyond online reviews.

What are the potential limitations or drawbacks of the Gibbs sampling approach used to extract rationales, and how could it be improved or replaced with alternative techniques

While Gibbs sampling is a powerful technique for approximating complex joint probability distributions, it has limitations that could impact the extraction of rationales in the context of opinion summarization. One drawback is the computational complexity of sampling from the joint distribution, which can be time-consuming for large datasets. Additionally, Gibbs sampling may struggle with high-dimensional spaces or when the number of possible combinations is vast, leading to inefficiencies in sampling. To address these limitations, alternative techniques like variational inference or Monte Carlo methods could be explored. These methods offer faster convergence and better scalability for large datasets, potentially improving the efficiency and effectiveness of rationale extraction.

How might the rationale-based summarization system be integrated into real-world applications, such as e-commerce platforms or travel booking sites, to enhance the user experience

Integrating the rationale-based summarization system into real-world applications like e-commerce platforms or travel booking sites can significantly enhance the user experience. In e-commerce, the system could provide concise summaries of product reviews with corresponding rationales, helping users make informed purchasing decisions. By highlighting key opinions and supporting evidence, users can quickly grasp the sentiment and reasons behind reviews. Similarly, in travel booking sites, the system could summarize hotel or destination reviews, showcasing popular opinions and reasons for recommendations. This feature could streamline the decision-making process for travelers and improve the overall user experience by providing valuable insights in a digestible format. Additionally, the system could offer personalized recommendations based on user preferences and past interactions, further enhancing user engagement and satisfaction.