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

arXiv: https://arxiv.org/abs/2006.04660 · HTML: https://mlsanity.com/paper/2006.04660/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Beyond Opinion Mining: Summarizing Opinions of Customer Reviews](https://mlsanity.com/paper/2206.01543/) (2022)
- [Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization](https://mlsanity.com/paper/2101.12406/) (2021)
- [On The Persona-based Summarization of Domain-Specific Documents](https://mlsanity.com/paper/2406.03986/) (2024)
- [Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts](https://mlsanity.com/paper/2405.06669/) (2024)
- [Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization](https://mlsanity.com/paper/2408.02584/) (2024)
- [LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reports](https://mlsanity.com/paper/2406.15809/) (2024)
- [A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data](https://mlsanity.com/paper/2505.09286/) (2025)

## Abstract (arXiv)

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.