Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information
Myungji Lee, Hongseok Kwon, Jaehun Shin, WonKee Lee, Baikjin Jung, Jong-Hyeok Lee
Abstract
Screenplay summarization is the task of extracting informative scenes from a screenplay. The screenplay contains turning point (TP) events that change the story direction and thus define the story structure decisively. Accordingly, this task can be defined as the TP identification task. We suggest using dialogue information, one attribute of screenplays, motivated by previous work that discovered that TPs have a relation with dialogues appearing in screenplays. To teach a model this characteristic, we add a dialogue feature to the input embedding. Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer. We observed that the model can better identify TPs in a screenplay by using dialogue information and that a model adopting Transformer outperforms LSTM-based models.- Anthology ID:
- 2021.nuse-1.6
- Volume:
- Proceedings of the Third Workshop on Narrative Understanding
- Month:
- June
- Year:
- 2021
- Address:
- Virtual
- Editors:
- Nader Akoury, Faeze Brahman, Snigdha Chaturvedi, Elizabeth Clark, Mohit Iyyer, Lara J. Martin
- Venues:
- NUSE | WNU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 56–61
- Language:
- URL:
- https://aclanthology.org/2021.nuse-1.6
- DOI:
- 10.18653/v1/2021.nuse-1.6
- Cite (ACL):
- Myungji Lee, Hongseok Kwon, Jaehun Shin, WonKee Lee, Baikjin Jung, and Jong-Hyeok Lee. 2021. Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information. In Proceedings of the Third Workshop on Narrative Understanding, pages 56–61, Virtual. Association for Computational Linguistics.
- Cite (Informal):
- Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information (Lee et al., NUSE-WNU 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.nuse-1.6.pdf
- Data
- TRIPOD