We proposed adding an explicit symbolic planning component to a neural data-to-text NLG system, which eases the burden on the neural component concerning text structuring and fact tracking.
while the plan-based system performs on par with a strong end-to-end neural system regarding automatic evaluation metrics and human fluency evaluation, it substantially outperforms the end-to-end system regarding faithfulness to the input.