MultiOpEd: A Corpus of Multi-Perspective News Editorials

Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth


Abstract
We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery. News editorial is a genre of persuasive text, where the argumentation structure is usually implicit. However, the arguments presented in an editorial typically center around a concise, focused thesis, which we refer to as their perspective. MultiOpEd aims at supporting the study of multiple tasks relevant to automatic perspective discovery, where a system is expected to produce a single-sentence thesis statement summarizing the arguments presented. We argue that identifying and abstracting such natural language perspectives from editorials is a crucial step toward studying the implicit argumentation structure in news editorials. We first discuss the challenges and define a few conceptual tasks towards our goal. To demonstrate the utility of MultiOpEd and the induced tasks, we study the problem of perspective summarization in a multi-task learning setting, as a case study. We show that, with the induced tasks as auxiliary tasks, we can improve the quality of the perspective summary generated. We hope that MultiOpEd will be a useful resource for future studies on argumentation in the news editorial domain.
Anthology ID:
2021.naacl-main.344
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4345–4361
Language:
URL:
https://aclanthology.org/2021.naacl-main.344
DOI:
10.18653/v1/2021.naacl-main.344
Bibkey:
Cite (ACL):
Siyi Liu, Sihao Chen, Xander Uyttendaele, and Dan Roth. 2021. MultiOpEd: A Corpus of Multi-Perspective News Editorials. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4345–4361, Online. Association for Computational Linguistics.
Cite (Informal):
MultiOpEd: A Corpus of Multi-Perspective News Editorials (Liu et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.344.pdf
Optional supplementary data:
 2021.naacl-main.344.OptionalSupplementaryData.zip
Optional supplementary code:
 2021.naacl-main.344.OptionalSupplementaryCode.zip
Video:
 https://aclanthology.org/2021.naacl-main.344.mp4
Code
 CogComp/MultiOpEd
Data
MultiOpEd