@inproceedings{lee-etal-2021-transformer,
title = "Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information",
author = "Lee, Myungji and
Kwon, Hongseok and
Shin, Jaehun and
Lee, WonKee and
Jung, Baikjin and
Lee, Jong-Hyeok",
editor = "Akoury, Nader and
Brahman, Faeze and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Iyyer, Mohit and
Martin, Lara J.",
booktitle = "Proceedings of the Third Workshop on Narrative Understanding",
month = jun,
year = "2021",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nuse-1.6",
doi = "10.18653/v1/2021.nuse-1.6",
pages = "56--61",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information
%A Lee, Myungji
%A Kwon, Hongseok
%A Shin, Jaehun
%A Lee, WonKee
%A Jung, Baikjin
%A Lee, Jong-Hyeok
%Y Akoury, Nader
%Y Brahman, Faeze
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Martin, Lara J.
%S Proceedings of the Third Workshop on Narrative Understanding
%D 2021
%8 June
%I Association for Computational Linguistics
%C Virtual
%F lee-etal-2021-transformer
%X 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.
%R 10.18653/v1/2021.nuse-1.6
%U https://aclanthology.org/2021.nuse-1.6
%U https://doi.org/10.18653/v1/2021.nuse-1.6
%P 56-61
Markdown (Informal)
[Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information](https://aclanthology.org/2021.nuse-1.6) (Lee et al., NUSE-WNU 2021)
ACL