  We introduce TVStoryGen, a story generation dataset that requires generating
detailed TV show episode recaps from a brief summary and a set of documents
describing the characters involved. Unlike other story generation datasets,
TVStoryGen contains stories that are authored by professional screen-writers
and that feature complex interactions among multiple characters. Generating
stories in TVStoryGen requires drawing relevant information from the lengthy
provided documents about characters based on the brief summary. In addition, we
propose to train reverse models on our dataset for evaluating the faithfulness
of generated stories. We create TVStoryGen from fan-contributed websites, which
allows us to collect 26k episode recaps with 1868.7 tokens on average.
Empirically, we take a hierarchical story generation approach and find that the
neural model that uses oracle content selectors for character descriptions
demonstrates the best performance on automatic metrics, showing the potential
of our dataset to inspire future research on story generation with constraints.
Qualitative analysis shows that the best-performing model sometimes generates
content that is unfaithful to the short summaries, suggesting promising
directions for future work.
