Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
Table 3: Performance comparisons of different methods for automatic review generation using three datasets.
['Datasets', 'Models', 'Perplexity', 'BLEU-1(%)', 'BLEU-4(%)', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Main Results. We can make the following observations. First, among the three context-based baselines, gC2S and Attr2Seq perform better than TransNets. The two models have similar network architectures, which are simpler than TransNets. We find they are easier to obtain a stable performance on large datasets. Second, GAN-based methods work better than the above baselines, especially LeakGAN. LeakGAN is specially designed for generating long text, and we adapt it to our task by incorporating context information. Third, ExpansionNet performs best among all the baseline models. A major reason is that it incorporates external knowledge such as review summaries, product titles and aspect keywords. Finally, our model outperforms all the baselines with a large margin. These baseline methods perform the generation in a single stage. As a comparison, we use a multi-stage process to gradually generate long and informative reviews in a coarse-to-fine way. Our model is able to better utilize aspect semantics and syntactic sketch, which is the key of the performance improvement over baselines. Overall, the three datasets show the similar findings. In what follows, we will report the results on Amazon data due to space limit. We select the best two baselines ExpansionNet and LeakGAN as reference methods.