  We introduce the first dataset for human edits of machine-generated visual
stories and explore how these collected edits may be used for the visual story
post-editing task. The dataset, VIST-Edit, includes 14,905 human edited
versions of 2,981 machine-generated visual stories. The stories were generated
by two state-of-the-art visual storytelling models, each aligned to 5
human-edited versions. We establish baselines for the task, showing how a
relatively small set of human edits can be leveraged to boost the performance
of large visual storytelling models. We also discuss the weak correlation
between automatic evaluation scores and human ratings, motivating the need for
new automatic metrics.
