  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.

Computation and Language