@inproceedings{roemmele-etal-2023-ablit,
title = "{A}b{L}it: A Resource for Analyzing and Generating Abridged Versions of {E}nglish Literature",
author = "Roemmele, Melissa and
Shaffer, Kyle and
Olsen, Katrina and
Wang, Yiyi and
DeNeefe, Steve",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.269",
doi = "10.18653/v1/2023.eacl-main.269",
pages = "3717--3733",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature
%A Roemmele, Melissa
%A Shaffer, Kyle
%A Olsen, Katrina
%A Wang, Yiyi
%A DeNeefe, Steve
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F roemmele-etal-2023-ablit
%X 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.
%R 10.18653/v1/2023.eacl-main.269
%U https://aclanthology.org/2023.eacl-main.269
%U https://doi.org/10.18653/v1/2023.eacl-main.269
%P 3717-3733
Markdown (Informal)
[AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://aclanthology.org/2023.eacl-main.269) (Roemmele et al., EACL 2023)
ACL