Multipart Spoiler

Multipart spoiler generation and detection are emerging research areas focused on mitigating the negative impact of spoilers, particularly in online contexts like movie reviews and clickbait articles. Current research employs various deep learning models, including large language models (LLMs) and transformer architectures, often within multi-task learning frameworks or ensemble methods, to generate concise summaries (spoilers) that address the curiosity provoked by clickbait or reveal plot points in reviews without excessive detail. This work is significant for improving online user experience by reducing the prevalence of unwanted spoilers and enhancing the effectiveness of clickbait countermeasures. The development of robust and accurate spoiler detection and generation systems is crucial for creating a more positive and informative online environment.