Data-to-Text Generation with Content Selection and Planning
This work addresses the challenge of producing coherent and relevant text from data for applications like automated reporting, though it is incremental as it builds on existing neural methods.
The paper tackles the problem of generating text from structured data by introducing a neural architecture that explicitly models content selection and planning while maintaining end-to-end training, resulting in improved state-of-the-art performance on the RotoWire dataset.
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.