paper-notion-summarizer

Fetch paper metadata by title or arXiv/DOI link, create a deep structured summary, and post it as a Notion page. The agent reads the paper and writes a seminar-quality summary adapted to the user's language.

Purpose

Given a paper title or arXiv link, create a seminar-quality, deeply structured summary and upload it to Notion.

⚠️ This is NOT an extractive summary. The agent reads the full paper and writes an original analysis.

Language Adaptation

Write the summary in the same language as the user's request.

• If the user writes in Korean → write in Korean (technical terms in English)
• If the user writes in English → write in English
• If the user writes in Japanese → write in Japanese
• And so on for any language.

Section headings in the summary JSON should match the user's language. The English template below is the canonical structure — adapt headings to the user's language.

Workflow (3 phases)

Phase 1: Extract paper content

bash

python3 scripts/extract_paper.py \
  --output /tmp/paper_extract.json \
  "https://arxiv.org/abs/2301.12345"

Or by title:

bash

python3 scripts/extract_paper.py \
  --output /tmp/paper_extract.json \
  --title "Attention Is All You Need"

Options:

• --output, -o: Output file path (defaults to stdout)
• --skip-fulltext: Extract abstract only (fast mode, skip PDF)
• --doi: Explicit DOI
• --arxiv-id: Explicit arXiv ID

Phase 2: Agent reads and writes the summary

Read the extracted JSON section by section (read tool with offset/limit for large files), then write a structured summary JSON to /tmp/paper_summary.json.

Reading strategy (context management)

• Read Abstract → Introduction → Method → Experiments → Conclusion in order
• For long papers, read in chunks and accumulate understanding
• Focus on: core idea, key equations, experimental setup, main results, ablations

Summary JSON template

json

{
  "title": "Paper Title (original language)",
  "metadata": {
    "authors": "Author list",
    "year": "2024",
    "venue": "NeurIPS 2024",
    "doi": "10.xxxx/xxxxx",
    "url": "https://arxiv.org/abs/xxxx.xxxxx",
    "source": "arXiv"
  },
  "sections": [
    {
      "heading": "0. Metadata",
      "content": "- Authors: ...\n- Year: ...\n- Venue: ...\n- Code: ..."
    },
    {
      "heading": "1. One-line Summary",
      "content": "What this paper does in one sentence."
    },
    {
      "heading": "2. Problem & Motivation",
      "content": "- What problem does it solve?\n- Why are existing methods insufficient?\n- Why is this research needed?"
    },
    {
      "heading": "3. Key Contributions",
      "content": "1. First contribution\n2. Second contribution\n3. Third contribution"
    },
    {
      "heading": "4. Method",
      "content": "Detailed pipeline/architecture description.\nCore ideas, key equations included.\n\n### Core Idea\n...\n\n### Architecture\n...\n\n### Training\n...\n\n### Key Equations\n$$equation$$"
    },
    {
      "heading": "5. Experiments",
      "content": "### Setup\n- Datasets: ...\n- Baselines: ...\n- Metrics: ...\n\n### Main Results\n- Key numbers and comparisons\n- Where it works and where it doesn't"
    },
    {
      "heading": "6. Ablation & Analysis",
      "content": "- Per-component contributions\n- Interesting analysis results\n- Hyperparameter sensitivity"
    },
    {
      "heading": "7. Limitations & Future Work",
      "content": "- Author-acknowledged limitations\n- Additional limitations you identify\n- Future research directions"
    },
    {
      "heading": "8. Overall Assessment",
      "content": "- Research significance\n- Strengths and weaknesses\n- Connections to related work\n- Ideas applicable to user's research"
    }
  ]
}

Quality guidelines

1. Terminology: Keep technical terms in their original language; explanations in the user's language.
2. Equations: Include key equations in LaTeX ($$ ... $$).
3. Depth: Seminar-presentation level understanding.
   • Method: Not just "they did X" but "why they designed it this way, what each component does"
   • Experiments: Not just "it worked" but "X% improvement over Y baseline under Z conditions"
4. Critical perspective: Record limitations and open questions, not just strengths.
5. Connections: If you know the user's research interests, connect the paper to them.
6. No programming code blocks: Do NOT use fenced code blocks ( ) in sections[*].content. Math expressions ($$ ... $$, ```latex) are allowed.
7. No emoji in headings: Use numbered prefixes: 0. Metadata, 1. One-line Summary, etc.

Phase 3: Push to Notion

bash

python3 scripts/push_to_notion.py \
  /tmp/paper_summary.json \
  --parent-page-id YOUR_PAGE_ID

Options:

• --parent-page-id: Notion page ID to create the summary under
• --force-update: Overwrite existing page with same title
• --dry-run: Preview without uploading
• --notion-key: Explicit Notion API token

Quick Start (full agent flow)

text

1. python3 scripts/extract_paper.py -o /tmp/paper_extract.json "https://arxiv.org/abs/..."
2. read /tmp/paper_extract.json (section by section)
3. Write summary → /tmp/paper_summary.json
4. python3 scripts/push_to_notion.py /tmp/paper_summary.json --parent-page-id PAGE_ID

Configuration

Config	Source	Description
Notion API key	NOTION_API_KEY env or ~/.config/notion/api_key	Required for Notion upload
Parent page	NOTION_PARENT_PAGE_ID env or --parent-page-id	Notion page to create summaries under

Notes

• arXiv papers use PDF extraction (requires pypdf). Install: pip install pypdf
• For very long papers (>100 pages), use --skip-fulltext and read HTML via web_fetch.
• Notion API version: 2025-09-03
• The extract_paper.py script does NOT require a Notion API key — it only fetches and extracts.