In this paper, we introduce AutoMV, an autonomous agent framework designed for generating real estate marketing videos. The framework integrates a diverse set of existing models into a tool library, allowing the agent to intelligently select and execute the appropriate tools. Given property images and text, the agent decomposes the task into manageable subtasks, generating storyline directives and corresponding camera movement trajectories to guide the video production process. By automatically applying video synthesis techniques and incorporating multimedia elements such as subtitles and background music, the agent transforms static real estate images into dynamic, visually appealing videos, thereby optimizing their impact for digital marketing purposes.

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely. As a result, methods for automatically producing content are valuable tools to address this information overload. We show that recent advances in pretrained language modeling can be combined for a two-stage extractive and abstractive approach for Wikipedia lead paragraph generation. We extend this approach to generate longer Wikipedia-style summaries with sections and examine how such methods struggle in this application through detailed studies with 100 reference human-collected surveys. This is the first study on utilizing web resources for long Wikipedia-style summaries to the best of our knowledge.