There is a high prevalence of disinformation and manipulative narratives in online news sources today, and verification of its informative integrity is a vital need as online audience is highly susceptible to being affected by such propaganda or disinformation. The task of verifying any online information is, however, a significant challenge. The task Multilingual Characterization and Extraction of Narratives from Online News, therefore focuses on developing novel methods of analyzing news ecosystems and detecting manipulation attempts to address this challenge. As a part of this effort, we focus on the subtask of Entity Framing, which involves assigning named entities in news articles one of three main roles ( Protagonist, Antagonist, and Innocent) with a further fine-grained role distinction. We propose a pipeline that involves summarizing the article with the summary being centered around the entity. The entity and its entity-centric summary is then used as input for a BERT-based classifier to carry out the final role classification. Finally, we experiment with different approaches in the steps of the pipeline and compare the results obtained by them.