document-memory-summarize

To summarize academic papers into Markdown memory, feed extracted document text into the tool to generate a concise memory file with fixed section headings for downstream reuse.

Converting document text to Markdown memory supports rebuttal workflows by generating a concise background memory block for Stage 2 and Stage 4 tasks, ensuring downstream processing has structured context.

The best way to extract text for memory summarization is using pypdf to capture the document text, which is then fed into the skill to produce a structured Markdown memory output.

The summarization tool outputs a strict JSON payload containing a markdown string, ensuring the summarized memory with mandatory section headings is structurally valid for downstream processing.

This Markdown memory generation is designed specifically for academic documents to support rebuttal workflows. Using it for non-academic contexts may not yield the intended fixed section headings.

To avoid fabrication when generating a concise memory, the tool produces a strict JSON payload with mandatory section headings derived strictly from the provided document text without inventing content.