# Informative and Controllable Opinion Summarization

arXiv: https://arxiv.org/abs/1909.02322 · HTML: https://mlsanity.com/paper/1909.02322/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 27 of 27)

- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [Learning Opinion Summarizers by Selecting Informative Reviews](https://mlsanity.com/paper/2109.04325/) (2021)
- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [EntSUM: A Data Set for Entity-Centric Summarization](https://mlsanity.com/paper/2204.02213/) (2022)
- [Readability Controllable Biomedical Document Summarization](https://mlsanity.com/paper/2210.04705/) (2022)
- [Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation](https://mlsanity.com/paper/2305.14647/) (2023)
- [OpinSummEval: Revisiting Automated Evaluation for Opinion Summarization](https://mlsanity.com/paper/2310.18122/) (2023)
- [Do Multi-Document Summarization Models Synthesize?](https://mlsanity.com/paper/2301.13844/) (2023)
- [Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery](https://mlsanity.com/paper/2303.01962/) (2023)
- [Beyond Opinion Mining: Summarizing Opinions of Customer Reviews](https://mlsanity.com/paper/2206.01543/) (2022)
- [Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization](https://mlsanity.com/paper/2410.14545/) (2024)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews](https://mlsanity.com/paper/2406.07359/) (2024)
- [Aligning Large Language Models with Diverse Political Viewpoints](https://mlsanity.com/paper/2406.14155/) (2024)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Self-Agreement: A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions](https://mlsanity.com/paper/2305.11460/) (2023)
- [Nutribullets Hybrid: Multi-document Health Summarization](https://mlsanity.com/paper/2104.03465/) (2021)
- [Generating Related Work](https://mlsanity.com/paper/2104.08668/) (2021)
- [Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2306.04787/) (2023)
- [Summarization of Opinionated Political Documents with Varied Perspectives](https://mlsanity.com/paper/2411.04093/) (2024)
- [Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization](https://mlsanity.com/paper/2303.11660/) (2023)
- [A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization](https://mlsanity.com/paper/2305.08503/) (2023)
- [Polarity Calibration for Opinion Summarization](https://mlsanity.com/paper/2404.01706/) (2024)
- [Fine-Grained Opinion Summarization with Minimal Supervision](https://mlsanity.com/paper/2110.08845/) (2021)
- [Faithful Summarisation under Disagreement via Belief-Level Aggregation](https://mlsanity.com/paper/2601.04889/) (2026)
- [Abstractive Opinion Tagging](https://mlsanity.com/paper/2101.06880/) (2021)
- [Comparative Snippet Generation](https://mlsanity.com/paper/2206.05473/) (2022)

## Abstract (arXiv)

Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for each target can be prohibitively large, neural network-based methods follow a two-stage approach where an extractive step first pre-selects a subset of salient opinions and an abstractive step creates the summary while conditioning on the extracted subset. However, the extractive model leads to loss of information which may be useful depending on user needs. In this paper we propose a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries. The framework enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model. We showcase an effective instantiation of our framework which produces more informative summaries and also allows to take user preferences into account using our zero-shot customization technique. Experimental results demonstrate that our model improves the state of the art on the Rotten Tomatoes dataset and generates customized summaries effectively.