Data-to-Text Generation with Content Selection and Planning

Understand

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.