  Existing data-driven methods can well handle short text generation. However,
when applied to the long-text generation scenarios such as story generation or
advertising text generation in the commercial scenario, these methods may
generate illogical and uncontrollable texts. To address these aforementioned
issues, we propose a graph-based grouping planner(GGP) following the idea of
first-plan-then-generate. Specifically, given a collection of key phrases, GGP
firstly encodes these phrases into an instance-level sequential representation
and a corpus-level graph-based representation separately. With these two
synergic representations, we then regroup these phrases into a fine-grained
plan, based on which we generate the final long text. We conduct our
experiments on three long text generation datasets and the experimental results
reveal that GGP significantly outperforms baselines, which proves that GGP can
control the long text generation by knowing how to say and in what order.
