News Editorials: Towards Summarizing Long Argumentative Texts

Shahbaz Syed, Roxanne El Baff, Johannes Kiesel, Khalid Al Khatib, Benno Stein, Martin Potthast


Abstract
The automatic summarization of argumentative texts has hardly been explored. This paper takes a further step in this direction, targeting news editorials, i.e., opinionated articles with a well-defined argumentation structure. With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials. We evaluate these summaries based on a tailored annotation scheme, where a high-quality summary is expected to be thesis-indicative, persuasive, reasonable, concise, and self-contained. Our corpus contains at least three high-quality summaries for about 90% of the editorials, rendering it a valuable resource for the development and evaluation of summarization technology for long argumentative texts. We further report details of both, an in-depth corpus analysis, and the evaluation of two extractive summarization models.
Anthology ID:
2020.coling-main.470
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5384–5396
Language:
URL:
https://aclanthology.org/2020.coling-main.470
DOI:
10.18653/v1/2020.coling-main.470
Bibkey:
Cite (ACL):
Shahbaz Syed, Roxanne El Baff, Johannes Kiesel, Khalid Al Khatib, Benno Stein, and Martin Potthast. 2020. News Editorials: Towards Summarizing Long Argumentative Texts. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5384–5396, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
News Editorials: Towards Summarizing Long Argumentative Texts (Syed et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.470.pdf