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 1 outperforms strong baselines improving the state-of-the-art on the recently released ROTOWIRE dataset.
Based on (Wiseman et al., 2017) , REF incorporates content selection and planing mechanisms into the encoder-decoder system and improves the stateof-the-art on the ROTOWIRE dataset.
Data-to-Text Generation with Content Selection and Planning
ArXiv
ArXiv
Computer Science