The CreativeSumm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes
Eunchong Kim, Taewoo Yoo, Gunhee Cho, Suyoung Bae, Yun-Gyung Cheong
Abstract
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.- Anthology ID:
- 2022.creativesumm-1.8
- Volume:
- Proceedings of The Workshop on Automatic Summarization for Creative Writing
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editor:
- Kathleen Mckeown
- Venue:
- CreativeSumm
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–56
- Language:
- URL:
- https://aclanthology.org/2022.creativesumm-1.8
- DOI:
- Cite (ACL):
- Eunchong Kim, Taewoo Yoo, Gunhee Cho, Suyoung Bae, and Yun-Gyung Cheong. 2022. The CreativeSumm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes. In Proceedings of The Workshop on Automatic Summarization for Creative Writing, pages 51–56, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- The CreativeSumm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes (Kim et al., CreativeSumm 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.creativesumm-1.8.pdf