Yun Gyung Cheong
Also published as: Yun-Gyung Cheong
The CreativeSumm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes
Eunchong Kim | Taewoo Yoo | Gunhee Cho | Suyoung Bae | Yun-Gyung Cheong
Proceedings of The Workshop on Automatic Summarization for Creative Writing
In this paper, we describe our work for the CreativeSumm 2022 Shared Task, Automatic Summarization for Creative Writing. The task is to summarize movie scripts, which is challenging due to their long length and complex format. To tackle this problem, we present a two-stage summarization approach using both the abstractive and an extractive summarization methods. In addition, we preprocess the script to enhance summarization performance. The results of our experiment demonstrate that the presented approach outperforms baseline models in terms of standard summarization evaluation metrics.
Prediction of a Movie’s Success From Plot Summaries Using Deep Learning Models
You Jin Kim | Yun Gyung Cheong | Jung Hoon Lee
Proceedings of the Second Workshop on Storytelling
As the size of investment for movie production grows bigger, the need for predicting a movie’s success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie’s performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work.
- You Jin Kim 1
- Jung Hoon Lee 1
- Eunchong Kim 1
- Taewoo Yoo 1
- Gunhee Cho 1
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