MultiOpEd: A Corpus of Multi-Perspective News Editorials

Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth
<span title="">2021</span> <i title="Association for Computational Linguistics"> Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies </i> &nbsp; <span class="release-stage">unpublished</span>
We propose MULTIOPED 1 , 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
more &raquo; ... ant to automatic perspective discovery, where a system is expected to produce a singlesentence 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.18653/v1/2021.naacl-main.344</a> <a target="_blank" rel="external noopener" href="">fatcat:s7ljtusz5zftnbjybyl5t73pge</a> </span>
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