Google’s well-known service, NotebookLM, which can generate audio recordings resembling podcasts from the information you provide, has recently launched Interactive mind maps to help users visually arrange and investigate data in their notebooks. This feature can create a branching diagram of main topics and subtopics based on the documents you upload. Refer to the example provided below for clarification.

In the feature, every mind map begins with a central concept (usually the notebook title or main theme) and expands into key ideas found throughout your notes — akin to an octopus’s tentacles. Your source materials are visually summarized by the system. It emphasizes the connections among concepts within a hierarchical node framework.

The company is currently in the process of launching the feature, which began on March 19.

AI/NLP Processing of Annotations and Node Creation

NotebookLM utilizes Google Gemini’s series of large language models (LLMs) to analyze the text for key concepts, topics, and their interrelations. Essentially, it carries out an outline or concept extraction: the model can be prompted to “summarize the key topics and subtopics found in these notes.” Every node in the mind map symbolizes a concept extracted from the content, while subnodes divide more detailed subtopics. For instance, if a student’s notebook focuses on the decline of coral reef ecosystems, the AI could produce top-level nodes such as “Ocean Acidification,” “Rising Sea Temperatures,” and “Pollution,” each with additional branches, as exemplified by Google on its website.

For accuracy, NotebookLM’s AI concentrates on grounded data from the user’s sources. It can check several documents in the notebook against each other to identify recurring themes. This can also show how various sources intersect and indicate whether they are in agreement or conflict.

Every node is connected to the content that motivated it. In other words, NotebookLM monitors the source or text section that backs up a particular node. This enables it to give context during your interactions with the node. After the AI has built the mind map data (topics, subtopics, connections), the presentation layer produces it as an interactive diagram.

You can also download the mind map as an image for sharing. In addition, nodes allow for interaction: when the cursor is placed over them, they may be highlighted or their source may be displayed; clicking on them initiates additional actions.

Extending nodes and integrating Q&A

A novel aspect of NotebookLM’s mind maps is their connection to the app’s AI assistant features. The mind map is connected to the NotebookLM chat interface, rather than being isolated. By clicking on a node, you can either pose a question to the AI or obtain pertinent information regarding that subject. When a user selects a node (for example, “Overfishing” on the coral reef map), NotebookLM will provide an immediate response in the chat panel, elucidating that subtopic with details sourced from the references. Essentially, this is a context-aware Q&A: the system is aware of which concept you clicked on, allowing it to either display a prepared summary or dynamically request an explanation from the LLM, limited to the relevant source material.

Examination with different knowledge mapping instruments

Tools for knowledge mapping can be easily obtained. As an example, there are multiple free alternatives in Python, including PyDot, Pymindmap, and NetworkX with Graphviz. Moreover, there are various Python libraries compatible with established mind mapping formats. Some examples are pymm, freeplane2md, and md2mm. However, the Notebook LM is more akin to other existing tools that come with an integrated UI. Notion serves as an all-in-one workspace for note-taking and AI-assisted writing; however, it lacks a built-in mind map view. Notion AI, regarding the incorporation of AI, is concentrated on producing content within notes but does not provide automatic visualizations of relationships. NotebookLM centers on mapping powered by AI.

Meanwhile, the notetaking application Obsidian includes a global network graph that displays the relationships among notes. This method stands apart from NotebookLM’s use of hierarchical mind maps to represent document contents. NotebookLM creates nodes and relationships automatically, whereas Obsidian’s connections are established manually via links. Moreover, while Obsidian’s graph view gives you a comprehensive overview of your entire vault, NotebookLM allows for a focused view of a specific set of sources. While Canvas by Obsidian enables the manual crafting of visual maps, it does not feature AI summarization.

Additionally, there is MindMeister, a specialized mind mapping application that allows users to add nodes manually and offers a wide range of formatting options. While it provides some AI-driven recommendations for new nodes, it does not create complete maps from documents automatically, as NotebookLM does.