  Recent approaches to data-to-text generation have adopted the very successful
encoder-decoder architecture or variants thereof. These models generate text
which is fluent (but often imprecise) and perform quite poorly at selecting
appropriate content and ordering it coherently. To overcome some of these
issues, we propose a neural model with a macro planning stage followed by a
generation stage reminiscent of traditional methods which embrace separate
modules for planning and surface realization. Macro plans represent high level
organization of important content such as entities, events and their
interactions; they are learnt from data and given as input to the generator.
Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show
that our approach outperforms competitive baselines in terms of automatic and
human evaluation.
