We follow the step-by-step approach to neural data-to-text generation we proposed in <cite>Moryossef et al. (2019)</cite> , in which the generation process is divided into a text-planning stage followed by a plan-realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than <cite>the original planner</cite>; (2) we incorporate typing hints that improve <cite>the model</cite>'s ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module.