Many learn a lot yet retain little. Often, this stems not from a lack of effort, but from flawed input methods. Textbooks, course slides and recordings follow a linear structure, whereas the human brain stores knowledge through networked connections — this is the root cause of the inefficiency of note-copying summaries.
Starting from this contradiction, this paper argues that knowledge summarization is essentially a process of “structural translation”. It sets four evaluation criteria: compression, layering, association and retrievability. It then compares four mainstream forms of summarization and states that “different types of knowledge call for different types of diagrams”. Building on this, it breaks down five reusable scenarios with ProcessOn’s capabilities, including AI-generated mind maps, structure conversion from documents and audio, flowcharts, timelines and fishbone diagrams. It also provides a complete implementation workflow: a four-step method for students and three categories of deliverables for teachers.