  The neural boom that has sparked natural language processing (NLP) research
through the last decade has similarly led to significant innovations in
data-to-text generation (DTG). This survey offers a consolidated view into the
neural DTG paradigm with a structured examination of the approaches, benchmark
datasets, and evaluation protocols. This survey draws boundaries separating DTG
from the rest of the natural language generation (NLG) landscape, encompassing
an up-to-date synthesis of the literature, and highlighting the stages of
technological adoption from within and outside the greater NLG umbrella. With
this holistic view, we highlight promising avenues for DTG research that not
only focus on the design of linguistically capable systems but also systems
that exhibit fairness and accountability.
