From Text to Context: An Entailment Approach for News Stakeholder Classification

TL;DR

This paper presents a novel natural language inference approach to classify news stakeholders, enhancing accuracy and enabling zero-shot detection across diverse news articles.

Contribution

It introduces a new method transforming stakeholder classification into an entailment task, leveraging contextual and external knowledge for improved detection.

Findings

Effective stakeholder classification in news articles.

Model performs well in zero-shot scenarios.

Outperforms existing methods in accuracy.

Abstract

Navigating the complex landscape of news articles involves understanding the various actors or entities involved, referred to as news stakeholders. These stakeholders, ranging from policymakers to opposition figures, citizens, and more, play pivotal roles in shaping news narratives. Recognizing their stakeholder types, reflecting their roles, political alignments, social standing, and more, is paramount for a nuanced comprehension of news content. Despite existing works focusing on salient entity extraction, coverage variations, and political affiliations through social media data, the automated detection of stakeholder roles within news content remains an underexplored domain. In this paper, we bridge this gap by introducing an effective approach to classify stakeholder types in news articles. Our method involves transforming the stakeholder classification problem into a natural language inference task, utilizing contextual information from news articles and external knowledge to enhance the accuracy of stakeholder type detection. Moreover, our proposed model showcases efficacy in zero-shot settings, further extending its applicability to diverse news contexts.