In today's rapidly evolving world, maintaining a comprehensive overview of the future landscape is essential for staying competitive and making informed decisions. However, given the large volume of daily news, manually obtaining a thorough overview of an entity's future prospects is quite challenging. To address this, we present a system designed to automatically extract and summarize future-related information of a queried entity from news articles. Our approach utilizes a novel and publicly accessible multi-source dataset comprising 6,800 annotated sentences to fine-tune a language model to identify future-related sentences. We then use topic modeling to extract the main topics from the data and rank them by relevance as well as present them on an interactive timeline. User evaluations have shown that the timelines and summaries our system produces are useful. The system is available as a web application at: https://chronicle2050.regevson.com.