Data-to-Text Generation with Content Selection and Planning


  • Ratish Puduppully University of Edinburgh
  • Li Dong University of Edinburgh
  • Mirella Lapata University of Edinburgh



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 model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.




How to Cite

Puduppully, R., Dong, L., & Lapata, M. (2019). Data-to-Text Generation with Content Selection and Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6908-6915.



AAAI Technical Track: Natural Language Processing