Data-to-text generation is very essential and important in machine writing applications. The recent deep learning models, like Recurrent Neural Networks (RNNs), have shown a bright future for relevant text generation tasks. However, rare work has been done for automatic generation of long reviews from user opinions. In this paper, we introduce a deep neural network model to generate long Chinese reviews from aspect-sentiment scores representing users' opinions. We conduct our study within the framework of encoderdecoder networks, and we propose a hierarchical structure with aligned attention in the Long-Short Term Memory (LSTM) decoder. Experiments show that our model outperforms retrieval based baseline methods, and also beats the sequential generation models in qualitative evaluations.
Beyond dialogue generation, REF proposes to generate long reviews from aspect-sentiment scores according to topic phrases.
Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores
INLG
Computer Science