# Aspect-Controllable Opinion Summarization

arXiv: https://arxiv.org/abs/2109.03171 · HTML: https://mlsanity.com/paper/2109.03171/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [Extractive Opinion Summarization in Quantized Transformer Spaces](https://mlsanity.com/paper/2012.04443/) (2020)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Unsupervised Opinion Summarization as Copycat-Review Generation](https://mlsanity.com/paper/1911.02247/) (2019)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [Neural Network-Based Abstract Generation for Opinions and Arguments](https://mlsanity.com/paper/1606.02785/) (2016)
- [Unsupervised Opinion Summarization with Noising and Denoising](https://mlsanity.com/paper/2004.10150/) (2020)
- [Unsupervised Opinion Summarization with Content Planning](https://mlsanity.com/paper/2012.07808/) (2020)
- [Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://mlsanity.com/paper/1711.09645/) (2017)
- [Self-Supervised and Controlled Multi-Document Opinion Summarization](https://mlsanity.com/paper/2004.14754/) (2020)
- [End-to-End Content and Plan Selection for Data-to-Text Generation](https://mlsanity.com/paper/1810.04700/) (2018)
- [Inference Time Style Control for Summarization](https://mlsanity.com/paper/2104.01724/) (2021)
- [Weakly Supervised Domain Detection](https://mlsanity.com/paper/1907.11499/) (2019)

## Built on by (most cited first, 30 of 47)

- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://mlsanity.com/paper/2210.12467/) (2022)
- [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://mlsanity.com/paper/2306.07799/) (2023)
- [Prompted Opinion Summarization with GPT-3.5](https://mlsanity.com/paper/2211.15914/) (2022)
- [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)
- [Comparative Opinion Summarization via Collaborative Decoding](https://mlsanity.com/paper/2110.07520/) (2021)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Unsupervised Extractive Opinion Summarization Using Sparse Coding](https://mlsanity.com/paper/2203.07921/) (2022)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization](https://mlsanity.com/paper/2410.13961/) (2024)
- [Efficient Few-Shot Fine-Tuning for Opinion Summarization](https://mlsanity.com/paper/2205.02170/) (2022)
- [OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization](https://mlsanity.com/paper/2312.04440/) (2023)
- [Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation](https://mlsanity.com/paper/2305.14647/) (2023)
- [MACSum: Controllable Summarization with Mixed Attributes](https://mlsanity.com/paper/2211.05041/) (2022)
- [Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias](https://mlsanity.com/paper/2402.00322/) (2024)
- [Do Multi-Document Summarization Models Synthesize?](https://mlsanity.com/paper/2301.13844/) (2023)
- [Beyond Opinion Mining: Summarizing Opinions of Customer Reviews](https://mlsanity.com/paper/2206.01543/) (2022)
- [Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately](https://mlsanity.com/paper/2301.11682/) (2023)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Hierarchical Indexing for Retrieval-Augmented Opinion Summarization](https://mlsanity.com/paper/2403.00435/) (2024)
- [Attributable and Scalable Opinion Summarization](https://mlsanity.com/paper/2305.11603/) (2023)
- [Product Description and QA Assisted Self-Supervised Opinion Summarization](https://mlsanity.com/paper/2404.05243/) (2024)
- [GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews](https://mlsanity.com/paper/2406.07359/) (2024)
- [Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection](https://mlsanity.com/paper/2111.07935/) (2021)
- [Unsupervised Opinion Summarization Using Approximate Geodesics](https://mlsanity.com/paper/2209.07496/) (2022)
- [Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://mlsanity.com/paper/2402.10554/) (2024)
- [STRUM-LLM: Attributed and Structured Contrastive Summarization](https://mlsanity.com/paper/2403.19710/) (2024)
- [OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews](https://mlsanity.com/paper/2509.00285/) (2025)
- [Noisy Pairing and Partial Supervision for Stylized Opinion Summarization](https://mlsanity.com/paper/2211.08723/) (2022)

## Abstract (arXiv)

Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them. In this paper, we propose an approach that allows the generation of customized summaries based on aspect queries (e.g., describing the location and room of a hotel). Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers which are induced by a multi-instance learning model that predicts the aspects of a document at different levels of granularity. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers. Experiments on two benchmarks show that our model outperforms the previous state of the art and generates personalized summaries by controlling the number of aspects discussed in them.