# A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

arXiv: https://arxiv.org/abs/2006.01592 · HTML: https://mlsanity.com/paper/2006.01592/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy](https://mlsanity.com/paper/2106.13945/) (2021)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation](https://mlsanity.com/paper/2305.14647/) (2023)
- [A Sentiment Consolidation Framework for Meta-Review Generation](https://mlsanity.com/paper/2402.18005/) (2024)
- [Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately](https://mlsanity.com/paper/2301.11682/) (2023)
- [Exploring Multitask Learning for Low-Resource AbstractiveSummarization](https://mlsanity.com/paper/2109.08565/) (2021)
- [PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models](https://mlsanity.com/paper/2410.03905/) (2024)
- [PerSEval: Assessing Personalization in Text Summarizers](https://mlsanity.com/paper/2407.00453/) (2024)
- [A Condense-then-Select Strategy for Text Summarization](https://mlsanity.com/paper/2106.10468/) (2021)
- [Abstractive Opinion Tagging](https://mlsanity.com/paper/2101.06880/) (2021)

## Abstract (arXiv)

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.