Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews

TL;DR

This paper introduces a personalized, aspect-based opinion summarization method for tourist reviews, allowing users to control summary attributes, using an unsupervised aspect extraction and ILP-based opinion selection, evaluated with crowdsourcing and ROUGE metrics.

Contribution

It presents a novel unsupervised approach combined with ILP for controllable, personalized summarization of tourist reviews, addressing user preferences often ignored in multi-document summarization.

Findings

Achieved competitive ROUGE scores in evaluation.

Enabled user control over summary length and aspects.

Demonstrated effectiveness through crowdsourcing experiments.

Abstract

Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process. Summaries, on the other hand, help readers with limited time budgets to quickly consume the key ideas from the data. State-of-the-art approaches for multi-document summarization, however, do not consider user preferences while generating summaries. In this work, we argue the need and propose a solution for generating personalized aspect-based opinion summaries from large collections of online tourist reviews. We let our readers decide and control several attributes of the summary such as the length and specific aspects of interest among others. Specifically, we take an unsupervised approach to extract coherent aspects from tourist reviews posted on TripAdvisor. We then propose an Integer Linear Programming (ILP) based extractive technique to select an informative subset of opinions around the identified aspects while respecting the user-specified values for various control parameters. Finally, we evaluate and compare our summaries using crowdsourcing and ROUGE-based metrics and obtain competitive results.