# Fine-Grained Opinion Summarization with Minimal Supervision

arXiv: https://arxiv.org/abs/2110.08845 · HTML: https://mlsanity.com/paper/2110.08845/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Extractive Opinion Summarization in Quantized Transformer Spaces](https://mlsanity.com/paper/2012.04443/) (2020)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [OpinionDigest: A Simple Framework for Opinion Summarization](https://mlsanity.com/paper/2005.01901/) (2020)
- [Informative and Controllable Opinion Summarization](https://mlsanity.com/paper/1909.02322/) (2019)
- [Self-Supervised and Controlled Multi-Document Opinion Summarization](https://mlsanity.com/paper/2004.14754/) (2020)
- [Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding](https://mlsanity.com/paper/2007.09536/) (2020)
- [W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/1705.07687/) (2017)
- [Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding](https://mlsanity.com/paper/2010.06705/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Multi-Granularity Summarization](https://mlsanity.com/paper/2201.12502/) (2022)

## Abstract (arXiv)

Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for identifying opinions. In this work, we propose a new framework, FineSum, which advances this frontier in three aspects: (1) minimal supervision, where only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis drills down to the sub-aspect level; and (3) phrase-based summarization, where opinion is summarized in the form of phrases. FineSum automatically identifies opinion phrases from the raw corpus, classifies them into different aspects and sentiments, and constructs multiple fine-grained opinion clusters under each aspect/sentiment. Each cluster consists of semantically coherent phrases, expressing uniform opinions towards certain sub-aspect or characteristics (e.g., positive feelings for ``burgers'' in the ``food'' aspect). An opinion-oriented spherical word embedding space is trained to provide weak supervision for the phrase classifier, and phrase clustering is performed using the aspect-aware contextualized embedding generated from the phrase classifier. Both automatic evaluation on the benchmark and quantitative human evaluation validate the effectiveness of our approach.