News Article Similarity

News article similarity research focuses on automatically determining how similar two news articles are, considering factors like narrative, entities, location, and time, across multiple languages. Current approaches leverage transformer-based Siamese networks and other deep learning architectures, often incorporating knowledge bases and techniques like knowledge distillation and data augmentation to improve accuracy and handle multilingual contexts. This research is crucial for applications such as media bias detection, event tracking across different news sources, and improving news recommendation systems, ultimately contributing to a deeper understanding of information dissemination and public perception.