@inproceedings{1da8449f61d042958d448b1dc42b4e6c,
title = "Plot and Rework: Modeling Storylines for Visual Storytelling",
abstract = "Writing a coherent and engaging story is not easy. Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and rework iteratively toward perfection. Automated visual storytelling (VIST) models, however, make poor use of external knowledge and iterative generation when attempting to create stories. This paper introduces PR-VIST, a framework that represents the input image sequence as a story graph in which it finds the best path to form a storyline. PR-VIST then takes this path and learns to generate the final story via a re-evaluating training process. This framework produces stories that are superior in terms of diversity, coherence, and humanness, per both automatic and human evaluations. An ablation study shows that both plotting and reworking contribute to the model's superiority.",
author = "Hsu, {Chi Yang} and Chu, {Yun Wei} and Huang, {Ting Hao} and Ku, {Lun Wei}",
note = "Funding Information: This research is supported by Ministry of Science and Technology, Taiwan under the project contract 108-2221-E-001-012-MY3 and 108-2923-E-001-001-MY2 and the Seed Grant from the College of Information Sciences and Technology (IST), Pennsylvania State University. We also thank the crowd workers for participating in this project. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics; Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 ; Conference date: 01-08-2021 Through 06-08-2021",
year = "2021",
language = "English (US)",
series = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
publisher = "Association for Computational Linguistics (ACL)",
pages = "4443--4453",
editor = "Chengqing Zong and Fei Xia and Wenjie Li and Roberto Navigli",
booktitle = "Findings of the Association for Computational Linguistics",
}