MultiOpEd: A Corpus of Multi-Perspective News Editorials

Dan Roth; Sihao Chen; Siyi Liu; Xander Uyttendaele

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MultiOpEd: A Corpus of Multi-Perspective News Editorials

Siyi Liu, Sihao Chen, Xander Uyttendaele, Dan Roth

Computation and Language · Submitted 4 Jun 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that, with the induced tasks as auxiliary tasks, we can improve the quality of the perspective summary generated.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,845 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.22 · human participants 0.10 · controlled comparison 0.18 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.12 · mentions code or data availability 0.66.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	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.	contribution 1.00	0.83	0.13	asserted 0.99
s1	News editorial is a genre of persuasive text, where the argumentation structure is usually implicit.	background 1.00	0.05	0.02	asserted 0.89
s2	However, the arguments presented in an editorial typically center around a concise, focused thesis, which we refer to as their perspective.	background 0.98	0.11	0.05	asserted 0.83
s3	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.	contribution 0.91	0.31	0.08	asserted 0.93
s4	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.	interpretation 0.35	0.14	0.23	asserted 0.98
s5	We first discuss the challenges and define a few conceptual tasks towards our goal.	contribution 0.65	0.71	0.02	unclear 0.53
s6	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.	contribution 0.65	0.92	0.06	asserted 0.97
s7	We show that, with the induced tasks as auxiliary tasks, we can improve the quality of the perspective summary generated.	result 1.00	0.95	0.38	asserted 1.00
s8	We hope that MultiOpEd will be a useful resource for future studies on argumentation in the news editorial domain.	future work 0.99	0.05	0.08	hedged possibility 0.77

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.80.

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original 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.

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1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Cites it as background 1 paper

Earlier work studied this issue of conflicting contexts through perturbations with entity-substitution [ref] , machine-generation [ref] , rule-based templates [ref] , or on controversial, multi-perspective questions [ref] .

Open Domain Question Answering with Conflicting Contexts · Introduction · 0.88

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Uses the method or tool of 1 paper

To address this, we adopt similar strategies as in knowledge distillation [ref] .

Distilling the Knowledge in a Neural Network · Multi-Task Framework · 0.94

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