Data-to-Text Generation with Content Selection and Planning
TL
TL;DR
This work presents a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training and shows that this model outperforms strong baselines improving the state-of-the-art on the recently released RotoWire dataset.
Abstract
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.
References45 items
1
Bootstrapping Generators from Noisy Data
2
Table-to-text Generation by Structure-aware Seq2seq Learning
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Order-Planning Neural Text Generation From Structured Data
4
Challenges in Data-to-Document Generation
5