@inproceedings{zang-wan-2017-towards,
    title = "Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores",
    author = "Zang, Hongyu  and
      Wan, Xiaojun",
    editor = "Alonso, Jose M.  and
      Bugar{\'i}n, Alberto  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-3526/",
    doi = "10.18653/v1/W17-3526",
    pages = "168--177",
    abstract = "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 encoder-decoder 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."
}