AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature
Melissa Roemmele, Kyle Shaffer, Katrina Olsen, Yiyi Wang, Steve DeNeefe
Abstract
Creating an abridged version of a text involves shortening it while maintaining its linguistic qualities. In this paper, we examine this task from an NLP perspective for the first time. We present a new resource, AbLit, which is derived from abridged versions of English literature books. The dataset captures passage-level alignments between the original and abridged texts. We characterize the linguistic relations of these alignments, and create automated models to predict these relations as well as to generate abridgements for new texts. Our findings establish abridgement as a challenging task, motivating future resources and research. The dataset is available at github.com/roemmele/AbLit.- Anthology ID:
- 2023.eacl-main.269
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3717–3733
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.269
- DOI:
- 10.18653/v1/2023.eacl-main.269
- Cite (ACL):
- Melissa Roemmele, Kyle Shaffer, Katrina Olsen, Yiyi Wang, and Steve DeNeefe. 2023. AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3717–3733, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature (Roemmele et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.eacl-main.269.pdf