Paper Abstract Summarizer
Use when user provides a paper abstract (text, PDF, or URL) and wants a structured summary, translation, or critical analysis.
Trigger phrases
- "总结这篇论文" / "summarize this paper"
- "帮我看看这篇摘要" / "extract abstract"
- "论文翻译" / "translate this abstract"
- "分析这篇论文" / "analyze this paper"
Workflow
1. Input handling
- Text: Read abstract directly from user message.
- PDF/ArXiv URL: Use
web_fetchto get the abstract page; if PDF, useexecwithpdftotextor Python pdfminer to extract the first page (where abstract usually lives). - DOI: Fetch from doi.org → redirect to publisher page.
2. Structured extraction
Extract these fields from the abstract:
| Field | Description | Required |
|---|---|---|
| Title | Full paper title | ✅ |
| Authors | Author names (if available) | ✅ |
| Venue | Conference/Journal, year | ⚠️ if present |
| Research Question | What problem is being solved | ✅ |
| Methodology | Approach, model, dataset, metrics | ✅ |
| Key Findings | Main results with numbers/stats | ✅ |
| Conclusions | Takeaway, limitations, future work | ✅ |
| Keywords | Listed keywords | ⚠️ if present |
3. Output format
Present in this structured Markdown:
## 📄 论文摘要总结
**标题:** [Title]
**作者:** [Authors]
**出处:** [Venue]
### 🎯 研究问题
[concise summary]
### 🔬 方法
[methodology summary]
### 📊 核心发现
- Finding 1 (with numbers if available)
- Finding 2
- ...
### 💡 结论与局限
[conclusions and limitations]
4. Optional post-processing
- 中文翻译: If user asks in Chinese or requests translation, add a
### 🌐 中文概要section with a fluent Chinese summary. - 批判性分析: If user asks "评价"/"analyze critically", add a
### ⚖️ 简评section: - Strengths of the approach
- Potential weaknesses / biases
- Relevance to AI/ML field
Hard constraints
- Never fabricate results, metrics, or citations. If the abstract doesn't contain certain info, mark as
N/A. - If the paper content cannot be accessed (paywall, broken link), say so clearly — do not guess the content.
- For Chinese users, default to bilingual output (English structured fields + Chinese summary).
Reference
- Common paper databases: arXiv, Semantic Scholar, Google Scholar, DBLP
- DOI resolution:
https://doi.org/{doi}