  Story ending generation is an interesting and challenging task, which aims to
generate a coherent and reasonable ending given a story context. The key
challenges of the task lie in how to comprehend the story context sufficiently
and handle the implicit knowledge behind story clues effectively, which are
still under-explored by previous work. In this paper, we propose a Story
Heterogeneous Graph Network (SHGN) to explicitly model both the information of
story context at different granularity levels and the multi-grained interactive
relations among them. In detail, we consider commonsense knowledge, words and
sentences as three types of nodes. To aggregate non-local information, a global
node is also introduced. Given this heterogeneous graph network, the node
representations are updated through graph propagation, which adequately
utilizes commonsense knowledge to facilitate story comprehension. Moreover, we
design two auxiliary tasks to implicitly capture the sentiment trend and key
events lie in the context. The auxiliary tasks are jointly optimized with the
primary story ending generation task in a multi-task learning strategy.
Extensive experiments on the ROCStories Corpus show that the developed model
achieves new state-of-the-art performances. Human study further demonstrates
that our model generates more reasonable story endings.
