Contextual content graph for automatic, unsupervised summarization of content

This invention describes a way to automatically summarize large amounts of text. It works by first analyzing the text to understand the meaning and connections between different phrases. Then, it builds a special map, or 'graph,' where each phrase is a point and lines connect related phrases, with the strength of the connection showing how similar they are, specifically by identifying themes and ranking phrases based on their relevance to these themes, to pick out the most important sentences for a summary.

Why it matters: Filed before the widespread adoption of large language models. Modern AI can now perform the complex semantic analysis and summarization steps with greater accuracy and efficiency.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, NLP algorithms, graph theory expertise

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