In recent work <cite>(Moryossef et al., 2019)</cite> <cite>we</cite> proposed to adopt ideas from "traditional" language generation approaches (i.e. Reiter and Dale (2000) ; Walker et al. (2007) ; Gatt and Krahmer (2017) ) that separate the generation into a planning stage that determines the order and structure of the expressed facts, and a realization stage that maps the plan to natural language text. <cite>We</cite> show that by breaking the task this way, one can achieve the same fluency of neural generation systems while being able to better control the form of the generated text and to improve its correctness by reducing missing facts and "hallucinations", common in neural systems.