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:
| Section | What to Look For |
|---|---|
| Title | Paper title from heading/first lines |
| Authors | Author names and affiliations |
| Venue | Conference/journal name, year |
| Abstract | The abstract paragraph |
| Problem | What problem does the paper address? |
| Methodology | Proposed approach, model architecture, algorithm |
| Experiments | Datasets, baselines, evaluation metrics |
| Results | Key quantitative/qualitative results |
| Conclusion | Main takeaways, limitations, future work |
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]
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
| Level | Detail | When |
|---|---|---|
| 🟢 Quick | Title + Problem + Main Result (3-5 lines) | User in a hurry |
| 🟡 Standard | Full structured summary with table | Default |
| 🔴 Deep Dive | Full summary + architecture + math + code notes | User asks for details |
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*
