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.
@article{arxiv.1809.00582,
title = {Data-to-Text Generation with Content Selection and Planning},
author = {Ratish Puduppully and Li Dong and Mirella Lapata},
journal= {arXiv preprint arXiv:1809.00582},
year = {2019}
}