---

🎯 What is SEAGraph?

Ever wondered what reviewers really mean when they write those cryptic comments on your paper? 🤔

SEAGraph is an intelligent framework that acts as your personal "review comment interpreter"! It doesn't just read review comments—it unveils the underlying intentions, context, and research landscape behind them.

🧠 How Does It Work?

SEAGraph constructs two powerful knowledge structures:

1. 📚 Semantic Mind Graph (SMG): Captures the author's thought process and the logical flow of the paper
2. 🌐 Hierarchical Background Graph (HBG): Maps out the research domains, related work, and academic context

By combining these graphs with intelligent retrieval, SEAGraph generates coherent, context-aware explanations that help you truly understand what reviewers are asking for!

---

🚀 Quick Start

Prerequisites

SEAGraph uses two separate environments due to dependency conflicts. Don't worry—we've got you covered!

🔬 Environment 1: For Nougat (PDF Parsing)

cuda==12.4
python==3.9.20
torch==2.5.1
transformers==4.38.2
nougat-ocr==0.1.17
numpy==2.0.2

🤖 Environment 2: For Mistral & Sentence-BERT (Graph Construction & Retrieval)

cuda==12.4
python==3.9.20
torch==2.5.1
vllm==0.6.4.post1
transformers==4.46.3
sentence-transformers==3.3.1
torch_cluster==1.6.3
torch_scatter==2.1.2
torch_sparse==0.6.18
torch_spline_conv==1.2.2

⚠️ Pro Tip: The Nougat environment may conflict with the Mistral environment. Consider using separate conda/virtual environments!

---

📂 Project Structure

SEAGraph/
├── 📄 data/
│   ├── paper_pdf/      # 🎓 Place your academic papers here (PDF format)
│   └── raw_review/     # 📝 Place your review comments here (TXT format)
├── 💻 code/            # 🛠️ All the magic happens here
├── 📊 result/          # ✨ Your explanations will appear here (JSON format)
└── 🎨 asset/           # 🖼️ Figures and visualizations

---

🎬 The SEAGraph Pipeline

Transform your paper and review comments into insightful explanations in 10 easy steps:

Step	Script	Description
1️⃣	pdf_parse.py	📖 Parse your PDF into machine-readable format (MMD)
2️⃣	smg.py	🧠 Construct the Semantic Mind Graph
3️⃣	review_process.py	🔍 Extract and process review comments
4️⃣	hbg_related_paper_search.py	📚 Search for related papers based on citations
5️⃣	hbg_themes_infer.py	🎯 Infer research themes and topics
6️⃣	hbg_hot_paper_search.py	🔥 Find trending papers in your field
7️⃣	hbg_process_paper.py	⚙️ Process all background papers
8️⃣	retrieve_smg.py	🎣 Retrieve relevant content from SMG
9️⃣	retrieve_hbg.py	🎣 Retrieve relevant content from HBG
🔟	rag_seagraph.py	🎉 Generate comprehensive explanations!

---

💡 Usage

It's as simple as running a single command!

python <script_name>.py --filename <your_paper_id>

Example: Process a Paper End-to-End

# Step 1: Parse the PDF
python pdf_parse.py --filename 5t44vPlv9x

# Step 2: Build the Semantic Mind Graph
python smg.py --filename 5t44vPlv9x

# Step 3: Process review comments
python review_process.py --filename 5t44vPlv9x

# ... continue with remaining steps ...

# Final step: Generate explanations
python rag_seagraph.py --filename 5t44vPlv9x

💡 Tip: Replace 5t44vPlv9x with your paper's unique identifier!

---

📊 Performance Insights

---

🤝 Contributing

We welcome contributions! Whether it's:

• 🐛 Bug reports
• 💡 Feature suggestions
• 📖 Documentation improvements
• 🔧 Code contributions

Feel free to open an issue or submit a pull request!

---

📜 Citation

If you find SEAGraph helpful in your research, please consider citing our work:

@article{yu2024seagraph,
  title={SEAGraph: Unveiling the Whole Story of Paper Review Comments},
  author={Yu, Jianxiang and Tan, Jiaqi and Ding, Zichen and Zhu, Jiapeng and Li, Jiahao and Cheng, Yao and Cui, Qier and Lan, Yunshi and Li, Xiang},
  journal={arXiv preprint arXiv:2412.11939},
  year={2024}
}

---

📧 Contact

Have questions? Reach out to us!

• 📮 Email: sea.ecnu@gmail.com