Sensationalism and Gender Bias in News Headlines using Fine-tuned Summarization Models

This project examines the relationship between a news article and its headline by understanding the importance of gender bias and sensational words. This build upon the article When Women Make Headlines where the authors compared the usage of sensational words in headlines about women to headlines about other topics. We extend their analysis by looking further into machine generated headlines and the articles themselves in order to understand potential biases in summarization models. [GitHub repository]