MultiOpEd: A Corpus of Multi-Perspective News Editorials
Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

TL;DR
MultiOpEd is a new open-domain corpus of news editorials designed to facilitate research on automatic perspective discovery and argumentation structure, enabling improved summarization and understanding of implicit arguments.
Contribution
The paper introduces MultiOpEd, a novel corpus supporting multiple tasks for automatic perspective discovery in news editorials, and demonstrates its utility through a multi-task learning approach for perspective summarization.
Findings
Multi-task learning improves perspective summary quality.
The corpus supports various argumentation analysis tasks.
Automatic perspective discovery is feasible with the proposed resources.
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Software Engineering Research
