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NotebookLM's Video Overviews now boast six vibrant visual styles from Google’s Nano Banana model, alongside two tailored formats for diverse viewer preferences, breathing life into video summaries like never before.
NotebookLM, Google's AI-powered document management and summarization tool, has rolled out significant updates to its Video Overviews feature. This latest iteration leverages the power of Google’s Nano Banana image editing and generation model (Gemini 2.5 Flash Image) to enhance both the content and visual appeal of video summaries.
The core technical changes in this update are:
These updates are particularly noteworthy for practitioners because they enhance the usability and engagement of video summaries, making them more versatile and visually appealing. Here’s a deeper dive into what changed:
NotebookLM now offers six new visual styles in addition to the classic style:
These styles are generated using Nano Banana, which can create "helpful, contextual, and beautiful illustrations based on the sources you upload." This integration means that the visual elements in your video overviews are not just aesthetically pleasing but also relevant to the content.
The new formats-Brief and Explainer-are designed to cater to different user needs:

To access these new features, follow these steps:
These updates are initially rolling out to NotebookLM Pro users (Google AI Pro) this week. Free users can expect these features to be available in the coming weeks.
On the broader front, Google has also announced official integrations of Nano Banana with other products:
For professionals who rely on NotebookLM for summarizing and presenting information, these updates offer several benefits:
NotebookLM's latest updates bring a fresh wave of features that enhance both the functionality and aesthetics of video overviews. By leveraging advanced AI models like Nano Banana, Google continues to push the boundaries of what is possible in document management and summarization tools.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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14 October 2025
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