  We study the problem of generating interesting endings for stories. Neural
generative models have shown promising results for various text generation
problems. Sequence to Sequence (Seq2Seq) models are typically trained to
generate a single output sequence for a given input sequence. However, in the
context of a story, multiple endings are possible. Seq2Seq models tend to
ignore the context and generate generic and dull responses. Very few works have
studied generating diverse and interesting story endings for a given story
context. In this paper, we propose models which generate more diverse and
interesting outputs by 1) training models to focus attention on important
keyphrases of the story, and 2) promoting generation of non-generic words. We
show that the combination of the two leads to more diverse and interesting
endings.
