Data-to-Text Generation with Content Selection and Planning

Code

• github.com/ratishsp/data2text-plan-py
  Officialpytorch★ 0
• github.com/jugalw13/Red-Hat-Hack
  none★ 0

Abstract

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model outperforms strong baselines improving the state-of-the-art on the recently released RotoWire dataset.

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Reproductions