# Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

arXiv: https://arxiv.org/abs/1906.05667 · HTML: https://mlsanity.com/paper/1906.05667/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Learning to Transfer Prompts for Text Generation](https://mlsanity.com/paper/2205.01543/) (2022)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Review-LLM: Harnessing Large Language Models for Personalized Review Generation](https://mlsanity.com/paper/2407.07487/) (2024)
- [Context-Tuning: Learning Contextualized Prompts for Natural Language Generation](https://mlsanity.com/paper/2201.08670/) (2022)
- [DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation](https://mlsanity.com/paper/2106.00791/) (2021)
- [Knowledge-based Review Generation by Coherence Enhanced Text Planning](https://mlsanity.com/paper/2105.03815/) (2021)
- [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://mlsanity.com/paper/2209.13885/) (2022)
- [User Review Writing via Interview with Dialogue Systems](https://mlsanity.com/paper/2603.07070/) (2026)
- [LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges](https://mlsanity.com/paper/2606.25057/) (2026)
- [Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation](https://mlsanity.com/paper/2110.10358/) (2021)
- [Few-Shot and Training-Free Review Generation via Conversational Prompting](https://mlsanity.com/paper/2509.20805/) (2025)

## Abstract (arXiv)

Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical and syntactic characteristics from natural languages. In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process. First, we model the aspect transitions to capture the overall content flow. Then, to generate a sentence, an aspect-aware sketch will be predicted using an aspect-aware decoder. Finally, another decoder fills in the semantic slots by generating corresponding words. Our approach is able to jointly utilize aspect semantics, syntactic sketch, and context information. Extensive experiments results have demonstrated the effectiveness of the proposed model.