TY - GEN
T1 - On how users edit computer-generated visual stories
AU - Hsu, Ting Yao
AU - Huang, Ting Hao Kenneth
AU - Hsu, Yen Chia
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/5/2
Y1 - 2019/5/2
N2 - 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.
AB - 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.
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U2 - 10.1145/3290607.3312965
DO - 10.1145/3290607.3312965
M3 - Conference contribution
AN - SCOPUS:85067299648
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
Y2 - 4 May 2019 through 9 May 2019
ER -