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

## TL;DR

This paper introduces an aspect-aware coarse-to-fine decoding model for generating long, informative reviews by capturing content flow, syntactic structure, and aspect semantics, outperforming previous word-level methods.

## Contribution

The paper presents a novel aspect-aware coarse-to-fine generation framework that jointly models aspect transitions, syntactic sketches, and semantic content for improved review generation.

## Key findings

- Effective in generating long, coherent reviews
- Outperforms previous word-level generation models
- Demonstrates strong results in experiments

## Abstract

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.