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.
BibTeX
@inproceedings{syed-etal-2020-news,
title = "News Editorials: Towards Summarizing Long Argumentative Texts",
author = "Syed, Shahbaz and
El Baff, Roxanne and
Kiesel, Johannes and
Al Khatib, Khalid and
Stein, Benno and
Potthast, Martin",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.470/",
doi = "10.18653/v1/2020.coling-main.470",
pages = "5384--5396"
}