  Goal-oriented Script Generation is a new task of generating a list of steps
that can fulfill the given goal. In this paper, we propose to extend the task
from the perspective of cognitive theory. Instead of a simple flat structure,
the steps are typically organized hierarchically - Human often decompose a
complex task into subgoals, where each subgoal can be further decomposed into
steps. To establish the benchmark, we contribute a new dataset, propose several
baseline methods, and set up evaluation metrics. Both automatic and human
evaluation verify the high-quality of dataset, as well as the effectiveness of
incorporating subgoals into hierarchical script generation. Furthermore, We
also design and evaluate the model to discover subgoal, and find that it is a
bit more difficult to decompose the goals than summarizing from segmented
steps.
