Student notes generator

Hi all — I’m Vruk. I built a small side project 2 months ago called Study Notes Generator (ChainSummary) and I’d love feedback, ideas, or testing help.

upload a long PDF / textbook / slide deck → get structured, readable study notes. Built using a custom pipeline I call PCS (Progressive Context Summarization) and runs with local LLMs via Ollama.

Repo:

https://github.com/xVrukx/Student-Notes-Generator

What it does

Multi-format input: PDF, DOCX, TXT, or raw text

Did prompt tuning (didn't have time to do fine tune and since I have other things to do)

Auto summarizes long documents into study-style notes

Interactive Q&A over the generated summary (ask clarifying questions)

Keeps intermediate files (Part1.txt, Part2.txt, …) althen combines at the end

How it works (flow / technique)

Upload & extract text from the file.

Chunking — default ~2,000 words per chunk.

Contextual summarization of each chunk with explicit linking prompts to preserve flow.

Progressive compression after every N chunks to reduce token usage while keeping core context.

Chained merging: merge chained summaries into Final_Summary.txt.

Refinement step for readability + optional Explanation Chat to ask questions or request clarifications.

This pipeline (PCS) is file-backed and traceable by design — you can inspect PartN.txt files to see exactly what was produced and why.

Why PCS (short)

Keeps continuity between chunks better than vanilla map-reduce flows.

Progressive compression helps scale to very long documents (20–30k+ words) on smaller token models.

File-backed chaining gives debuggability and reproducibility (easy to audit intermediate outputs).

Practical chunking & accuracy (estimates, Phi-3-mini-4K)

1,000–2,000 words → 1 chunk — ~95%

2,001–6,000 → 3 chunks — ~88–90%

6,001–12,000 → 6 chunks — ~82–85%

12,001–15,000 → 8 chunks — ~78–80%

15,001–20,000 → 10 chunks — ~72–75%

20,001–30,000 → 15 chunks — ~68–70%

(Using larger models improves accuracy.)

Limitations

More chunking required for small-token models.

Minor detail loss can occur across multi-stage compression.

Early errors can propagate forward — trace files help spot and fix this.

Quality depends on input (poor OCR / messy slides = harder job).

Tech stack & requirements

Frontend: Next.js + TypeScript + Tailwind CSS

LLM backend: Ollama (local) — model tested: Phi-3-mini-4K (GGUF compatible)

Prereqs: Node.js (>=18), Ollama installed locally

References / influences

Christensen et al., Hierarchical Summarization (ACL 2014)

LangChain — map-reduce summarization docs

Rong et al., EduFuncSum: Progressive Transformer for Code (2025)

Tiago Forte / Progressive Summarization ideas

If you have a moment, I’d love

Feedback on the README / UX / accuracy claims

Suggestions for better chunking or compression prompts

Ideas to handle visual PDFs (images, tables) better

Stars, issues, or PRs if you want to help improve