  A significant body of research in Artificial Intelligence (AI) has focused on
generating stories automatically, either based on prior story plots or input
images. However, literature has little to say about how users would receive and
use these stories. Given the quality of stories generated by modern AI
algorithms, users will nearly inevitably have to edit these stories before
putting them to real use. In this paper, we present the first analysis of how
human users edit machine-generated stories. We obtained 962 short stories
generated by one of the state-of-the-art visual storytelling models. For each
story, we recruited five crowd workers from Amazon Mechanical Turk to edit it.
Our analysis of these edits shows that, on average, users (i) slightly
shortened machine-generated stories, (ii) increased lexical diversity in these
stories, and (iii) often replaced nouns and their determiners/articles with
pronouns. Our study provides a better understanding on how users receive and
edit machine-generated stories,informing future researchers to create more
usable and helpful story generation systems.
