  Neural-based end-to-end approaches to natural language generation (NLG) from
structured data or knowledge are data-hungry, making their adoption for
real-world applications difficult with limited data. In this work, we propose
the new task of \textit{few-shot natural language generation}. Motivated by how
humans tend to summarize tabular data, we propose a simple yet effective
approach and show that it not only demonstrates strong performance but also
provides good generalization across domains. The design of the model
architecture is based on two aspects: content selection from input data and
language modeling to compose coherent sentences, which can be acquired from
prior knowledge. With just 200 training examples, across multiple domains, we
show that our approach achieves very reasonable performances and outperforms
the strongest baseline by an average of over 8.0 BLEU points improvement. Our
code and data can be found at \url{https://github.com/czyssrs/Few-Shot-NLG}
