name: paper-summarizer description: "Extract and summarize main content from academic papers. Parse abstract, methodology, results, and conclusions into structured summaries."

Paper Summarizer 🎓

Extract the core content from academic papers (PDF, arXiv, or text) into structured, easy-to-read summaries. Designed for undergraduate AI students who need to quickly understand papers for courses, projects, or literature reviews.

When to Use

• User provides a paper link (arXiv, PDF URL) or pasted text
• User asks "summarize this paper" or "what's the main idea of this paper"
• User needs a quick understanding of methodology/results before deciding to read further
• User is building a literature review for a course project

Workflow

Step 1: Fetch the Paper

If the user provides a URL (arXiv, PDF, etc.):

1. Use web_fetch to retrieve the paper page
2. For arXiv: fetch the abstract page (e.g., https://arxiv.org/abs/XXXX.XXXXX)
3. For direct PDF: use web_fetch with extractMode
4. If text is pasted directly, skip this step

Step 2: Parse Structure

Extract these sections from the paper content:

Step 3: Generate Structured Summary

Output in this format:

## 📄 Paper Summary

**Title**: [Title]
**Authors**: [Authors]
**Venue**: [Venue] (if available)

### 🎯 Problem
[1-2 sentences on what problem the paper solves]

### 🧠 Methodology
[2-4 sentences on the proposed approach. Include key formulas or architecture names but keep them high-level]

### 🔬 Key Results
| Metric | Proposed | Baseline | Improvement |
|--------|----------|----------|-------------|
| [Metric 1] | [Value] | [Value] | [Δ] |
| [Metric 2] | [Value] | [Value] | [Δ] |

### ✅ Conclusion & Takeaways
- **Key insight**: [One sentence]
- **Strength**: [One sentence]
- **Limitation**: [One sentence if mentioned]
- **Relevance**: [For AI undergrad: why this matters]
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Step 4: Optional — Deep Dive

If the user wants more detail, offer to expand:

- **Architecture diagram**: Describe the model architecture in text
- **Ablation studies**: What components matter most
- **Math breakdown**: Explain key formulas step by step
- **Implementation notes**: How to reproduce in PyTorch
- **Related work**: How this compares to similar papers
- **My take**: What this means for a 大三 AI student

Output Levels

Important Notes

• Don't hallucinate numbers: Only report results explicitly stated in the paper. If not found, say "Not specified in summary."
• Maintain academic tone: Objective, factual, no exaggerated claims
• Cite source: Always mention where the summary was extracted from (arXiv ID, URL, or source)
• Lead with relevance: Start with why a 大三 AI student should care about this paper

Example

User: "Summarize this paper: https://arxiv.org/abs/1706.03762"

Response (🟡 Standard):

## 📄 Paper Summary

**Title**: Attention Is All You Need
**Authors**: Vaswani et al.
**Venue**: NeurIPS 2017

### 🎯 Problem
RNN-based sequence models are slow to train due to sequential computation. This paper proposes Transformer, a model relying solely on attention mechanisms, eliminating recurrence entirely.

### 🧠 Methodology
Proposes the Transformer architecture built on multi-head self-attention and position-wise feed-forward networks. Uses positional encodings to retain sequence order information. Trained with a scaled dot-product attention formulation: Attention(Q,K,V) = softmax(QK^T/√d_k)V.

### 🔬 Key Results
| Metric | Transformer | SOTA | Improvement |
|--------|------------|------|-------------|
| BLEU (EN-DE) | 28.4 | 27.3 (Ensemble) | +1.1 |
| BLEU (EN-FR) | 41.0 | 38.1 | +2.9 |
| Training time | 3.5 days | 3.5+ days (Ensemble) | 10x+ faster |

### ✅ Conclusion & Takeaways
- **Key insight**: Pure attention is enough; recurrence is not necessary for sequence transduction.
- **Relevance**: Foundation of BERT, GPT, and all modern LLMs. Must-know for any AI student.

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*Summarized from: https://arxiv.org/abs/1706.03762*

這個論文摘要工具質量中上，文件寫得清晰易懂，提供了從簡到繁三種輸出級別，能滿足不同場景需求，示例完整便於上手。但它比較依賴網路獲取論文內容，如果遇到格式特殊的論文或網路問題可能效果不穩定。適合需要快速瞭解AI論文要點的學生使用，但建議配合原文核實關鍵資料。.