@inproceedings{zhao-etal-2022-narrasum,
title = "{N}arra{S}um: A Large-Scale Dataset for Abstractive Narrative Summarization",
author = "Zhao, Chao and
Brahman, Faeze and
Song, Kaiqiang and
Yao, Wenlin and
Yu, Dian and
Chaturvedi, Snigdha",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.14",
doi = "10.18653/v1/2022.findings-emnlp.14",
pages = "182--197",
abstract = "Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Writing a summary for a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narratives, which are collected from the synopses of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at \url{https://github.com/zhaochaocs/narrasum}.",
}
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<abstract>Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Writing a summary for a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narratives, which are collected from the synopses of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.</abstract>
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%0 Conference Proceedings
%T NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
%A Zhao, Chao
%A Brahman, Faeze
%A Song, Kaiqiang
%A Yao, Wenlin
%A Yu, Dian
%A Chaturvedi, Snigdha
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhao-etal-2022-narrasum
%X Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Writing a summary for a narrative is challenging as it requires an understanding of event causality and character behaviors. To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset. It contains 122K narratives, which are collected from the synopses of movies and TV episodes with diverse genres, and their corresponding abstractive summaries. Experiments show that there is a large performance gap between humans and the state-of-the-art summarization models on NarraSum. We hope that this dataset will promote future research in summarization, as well as broader studies of natural language understanding and generation. The dataset is available at https://github.com/zhaochaocs/narrasum.
%R 10.18653/v1/2022.findings-emnlp.14
%U https://aclanthology.org/2022.findings-emnlp.14
%U https://doi.org/10.18653/v1/2022.findings-emnlp.14
%P 182-197
Markdown (Informal)
[NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://aclanthology.org/2022.findings-emnlp.14) (Zhao et al., Findings 2022)
ACL