M ulti O p E d: A Corpus of Multi-Perspective News Editorials (2021)

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.

## introduction news editorial is a form of persuasive text that conveys consensus opinion on a controversial topic from the editors of a newspaper. much like an argumentative essay, a news editorial centers around a thesis, which represents the authors' perspective on the topic. usually, a news editorial argues in favor of the authors' stance on the topic, and is substantiated by extensive factual evidence. each editorial comes with a single paragraph abstract plus a one-sentence perspective, that abstractively summarizes the editorial's key argument in the context of the query. the two resulting perspectives serve as responses with opposite stance to the query. as news editorials function as professionally produced written discourse for conveying media attitude and guidance, they have traditionally been studied by the community as a rich resource for many argumantation-related tasks. @xcite @xcite . this work targets the problem of developing computational methods to identify and comparatively analyze the authors' perspectives and supporting arguments behind news editorials. one challenge to studying the argumentation structure in news editorials is that its elements are rarely expressed explicitly @xcite . for example, figure 1 shows two news editorials holding opposite views on whether a lockdown should continue. however, neither of them present their key perspectives explicitly. instead, the perspective is conveyed through subtle rhetoric strategies to either affirm or challenge the readers' stance from prior belief on the topic, as a study by @xcite discovers. as figure 1 shows, the statement "the lock down should stop" concisely summarizes the perspective expressed in the article on the left. we refer to such statements as "perspectives" throughout the paper. the ability to abstractively summarize the perspectives from the editorial would allow us to understand multiple topic-aligned editorials in context and reason about their inter-editorial argumentation structure. to facilitate research along the line, we collect data from theperspective foot_1 website, and construct multioped, an open-domain english news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery @xcite . the structure of the data is shown in figure 1 . for each of the 1,397 natural language query on a different topic in our dataset, it features two (rather long) news editorials. each editorial features a single-sentence perspective, which is abstractively summarized from the editorial by human experts. a short abstract then highlights the details in the editorial that support the perspective. the perspectives of the two editorials represents responses of opposite stances towards the query. naturally, the structure of the dataset induces a range of important argumentation-related natural language understanding tasks. for instance, the presence of the summary perspective allows for stance classification @xcite with respect to the query, which arguably is more tangible than inferring the stance from the entire editorial. another example task is the conditional generation of the perspective from the abstract/editorial, which relates to the widely studied task of argument generation @xcite . we defer the more detailed description of the induced tasks to section 3. one key advantage of multioped that is absent from earlier datasets is that a large number of argumentation-related tasks can be studied jointly using a single high quality corpus. to demonstrate this benefit and the utility of the multioped dataset foot_2 along with its induced tasks, we study the problem of perspective summarization in a multitask learning setting. we employ perspective relevance and stance classifications as two auxilliary tasks to the summarization objective. our empiri-cal and human analysis on the generated summaries show that the multi-task learning setting improves the generated perspectives in terms of the argument quality and stance consistency. in summary, our contributions in this work are three-fold. first, we propose a conceptual framework for identifying and abstracting the perspectives and the corresponding argumentation structure in news editorials, and define a set of tasks necessary for achieving this goal. second, we propose the multioped dataset, a news editorial dataset that induces multiple argumentation-related tasks. third, we demonstrate the utility of our multi-purpose dataset and induced tasks, by using the perspective summarization task as a case study. we include the induced tasks as auxiliary objectives in multi-task learning setting, and demonstrate their effectiveness to perspective summarization. ## design principles our goal of perspective discovery follows similar definition proposed by @xcite , and is closely related to a widely studied area of argumentation mining, i.e. identifying the argumentation structure within persuasive text @xcite . however, most studies in this domain focus on extractive methods, which becomes less applicable to our study. as the arguments are usually presented in an subtle and implicit way in news editorials, we instead focus on the generation methods for the perspectives. this closely resembles the argument conclusion generation task @xcite . one key distinction here is the presense of query to provide topic guidance during the perspective generation. compared to other conditional text generation tasks, perspective generation subjects to a few more constraints with respect to the argumentation structure. for example, the perspective must constitute the same stance (hasan and ng, 2013) as the editorial towards the query. on the other hand, while the editorial may cover content not directly related to the query, the generated perspective must present a relevant argument in the query's context. such structural constraints can be studied in the format of classification problems. and being able to study such problems along side the perspective summarization task on one high-quality corpus is important in our case, as it opens up the probability of modeling the tasks jointly. we show the benefit of ## multioped and induced tasks following the design principles outlined in the previous section, we propose a topic-aligned english news editorial corpus, multioped. the structure of an example instance in multioped is shown in figure 1 . to clarify our description of the dataset, we use the following notations. let q be a query about a controversial topic. each q in the dataset is paired with two editorials e pro and e con , that constitute supporting and opposing stances to the query q respectively. each editorial is abstracted into and a single-sentence perspective p, which provides a high-level summarization of the key argument presented in the editorial. the premises, or relevant details to support the perspective, forms the abstract a. naturally, the relation between these elements induces several tasks, most of which encompass similar definitions to existing argumentation-related tasks. we define and describe the tasks and their connection to our end goal of perspective discovery below. conceptually, this problem resembles the task of argument conclusion generation @xcite . we adopt a slightly different setting where the target topic is expressed in the form of a natural language query. ## conclusion we present multioped an open-domain news editorial corpus that induces a number of argumentation-related tasks. the proposed dataset presents a few properties that are absent from existing datasets. first, the elements in the annotation structure are presented as abstraction over the text in editorial, as such elements usually exist implicitly in editorials. second, as the pairs of editorials are aligned by topic, and exhibit opposing stance to each other, such structure allows for studies on cross-document argumentation structure. third, the dataset allows for the study of multiple argumentation-related tasks together. to demonstrate the power of having multiple related tasks in a single high-quality dataset, we study the problem of perspective summarization in a multi-task learning setting. our analysis shows that modeling stance and relevance classification jointly with the summarization task improves the overall quality of the perspective generated. in future work, we hope to utilize the corpus to improve the multi-task framework for perspective summarization. as we set aside the problem of abstract generation in our case study, we would also like to identify the challenges and potential solution to the problem. we hope that multioped presents opportunities and challenges to future research in argumentation.