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.
Cite
CITATION STYLE
Syed, S., El Baff, R., Al-Khatib, K., Kiesel, J., Stein, B., & Potthast, M. (2020). News Editorials: Towards Summarizing Long Argumentative Texts. In COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference (pp. 5384–5396). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.coling-main.470
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