  Datasets for data-to-text generation typically focus either on multi-domain,
single-sentence generation or on single-domain, long-form generation. In this
work, we create a large-scale dataset, WikiTableT, that pairs Wikipedia
sections with their corresponding tabular data and various metadata. WikiTableT
contains millions of instances, covering a broad range of topics, as well as a
variety of flavors of generation tasks with different levels of flexibility. We
benchmark several training and decoding strategies on WikiTableT. Our
qualitative analysis shows that the best approaches can generate fluent and
high quality texts but they sometimes struggle with coherence.
