Abstract: Implementations relate to utilizing generative model(s) to generate a personalized summary of content that is interactive. Processor(s) of a system can: select a plurality of sources of content to be utilized in generating the summary of the content, cause the summary of the content to be generated using the generative model(s), and cause the summary of the content to be rendered. In some implementations, the processor(s) can proactively determine to cause the summary of the content to be generated and rendered (e.g., based on one or more triggering criteria being satisfied). In other implementations, the processor(s) can reactively determine to cause the summary of the content to be generated and rendered (e.g., based on user input being received). In various implementations, while the summary of the content is being rendered, a user can interrupt the rendering of the summary of the content, and the processor(s) can handle the interruption accordingly.

Abstract: An application may generate tab information about tabs opened on a user device. An application may identify a tab group from the tabs, the tab group including at least two tabs determined to be related based on the tab information. An application may generate a label for the tab group based on at least a portion of the tab information. An application may modify a tab strip to include the label and the tab group.

Abstract: An application may receive a prompt from a user related to an input for a text field of digital content. An application may generate context data about the digital content. An application may provide the prompt and the context data to a generative language model. An application may receive a response generated by the generative language model and provide the response as a suggestion for the input for the text field.