BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization
Wojciech Kryscinski, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev
Abstract
The majority of existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future text summarization systems. We address these issues by introducing BOOKSUM, a collection of datasets for long-form narrative summarization. Our dataset covers documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.- Anthology ID:
- 2022.findings-emnlp.488
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6536–6558
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.488
- DOI:
- 10.18653/v1/2022.findings-emnlp.488
- Cite (ACL):
- Wojciech Kryscinski, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, and Dragomir Radev. 2022. BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6536–6558, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization (Kryscinski et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2022.findings-emnlp.488.pdf