class_docstr: @inproceedings{shao-etal-2019-long,
  title = "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model",
  author = "Shao, Zhihong  and Huang, Minlie  and Wen, Jiangtao  and Xu, Wenfei  and Zhu, Xiaoyan",
  booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
  month = nov,
  year = "2019",
  address = "Hong Kong, China",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/D19-1321",
  doi = "10.18653/v1/D19-1321",
  pages = "3257--3268",
  abstract = "Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.",
  }
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code: def get_templates(self):
      return [
          "关键词：{key_values} 目标：根据上述关键词信息，生成一段广告文案. 文案：",
      ]
  
imports: from .processor import *
label_desc: Use image node_id 3 for calling the E_reviews obj's underlying member method code with example usage: obj.get_templates() and returns: list