Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative. In this work, we revisit a widely used conceptualization of framing from the communication sciences which explicitly captures elements of narratives, including conflict and its resolution, and integrate it with the narrative framing of key entities in the story as heroes, victims or villains. We adapt an effective annotation paradigm that breaks a complex annotation task into a series of simpler binary questions, and present an annotated data set of English news articles, and a case study on the framing of climate change in articles from news outlets across the political spectrum. Finally, we explore automatic multi-label prediction of our frames with supervised and semi-supervised approaches, and present a novel retrieval-based method which is both effective and transparent in its predictions. We conclude with a discussion of opportunities and challenges for future work on document-level models of narrative framing.

翻译：尽管自然语言处理领域对媒体框架自动检测的兴趣日益增长，该问题通常被简化为单标签分类，并采用类似主题的框架观，回避了对更广泛的文档级叙事进行建模。本研究重新审视了传播学中一种广泛使用的框架概念化，该概念明确捕捉了叙事要素（包括冲突及其解决），并将其与故事中关键实体作为英雄、受害者或恶者的叙事框架相结合。我们改编了一种有效的标注范式，将复杂的标注任务分解为一系列更简单的二元问题，并提供了英文新闻文章的标注数据集，以及关于新闻机构（涵盖不同政治光谱）对气候变化框架构建的案例研究。最后，我们探索了通过有监督和半监督方法对框架进行自动多标签预测，并提出了一种新颖的基于检索的方法，该方法在预测中兼具有效性和透明性。我们以对文档级叙事框架建模的未来研究机遇与挑战的讨论作为结论。