  Systems for story generation are asked to produce plausible and enjoyable
stories given an input context. This task is underspecified, as a vast number
of diverse stories can originate from a single input. The large output space
makes it difficult to build and evaluate story generation models, as (1)
existing datasets lack rich enough contexts to meaningfully guide models, and
(2) existing evaluations (both crowdsourced and automatic) are unreliable for
assessing long-form creative text. To address these issues, we introduce a
dataset and evaluation platform built from STORIUM, an online collaborative
storytelling community. Our author-generated dataset contains 6K lengthy
stories (125M tokens) with fine-grained natural language annotations (e.g.,
character goals and attributes) interspersed throughout each narrative, forming
a robust source for guiding models. We evaluate language models fine-tuned on
our dataset by integrating them onto STORIUM, where real authors can query a
model for suggested story continuations and then edit them. Automatic metrics
computed over these edits correlate well with both user ratings of generated
stories and qualitative feedback from semi-structured user interviews. We
release both the STORIUM dataset and evaluation platform to spur more
principled research into story generation.

Computation and Language;Human-Computer Interaction