NEWTS: A Corpus for News Topic-Focused Summarization

Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff


Abstract
Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content. To date, all summarization datasets operate under a one-size-fits-all paradigm that may not reflect the full range of organic summarization needs. Several recently proposed models (e.g., plug and play language models) have the capacity to condition the generated summaries on a desired range of themes. These capacities remain largely unused and unevaluated as there is no dedicated dataset that would support the task of topic-focused summarization.This paper introduces the first topical summarization corpus NEWTS, based on the well-known CNN/Dailymail dataset, and annotated via online crowd-sourcing. Each source article is paired with two reference summaries, each focusing on a different theme of the source document. We evaluate a representative range of existing techniques and analyze the effectiveness of different prompting methods.
Anthology ID:
2022.findings-acl.42
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
493–503
Language:
URL:
https://aclanthology.org/2022.findings-acl.42
DOI:
10.18653/v1/2022.findings-acl.42
Bibkey:
Cite (ACL):
Seyed Ali Bahrainian, Sheridan Feucht, and Carsten Eickhoff. 2022. NEWTS: A Corpus for News Topic-Focused Summarization. In Findings of the Association for Computational Linguistics: ACL 2022, pages 493–503, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
NEWTS: A Corpus for News Topic-Focused Summarization (Bahrainian et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.42.pdf