AI Research Paper Summarizer & Reviewer using DeepSeek R1

Description

This project leverages DeepSeek R1 in combination with LangChain to automate the summarization and critical analysis of academic research papers. It extracts essential sections—like abstract, methodology, results, and conclusion—and generates clear, structured summaries. The system also provides reviewer-style critiques highlighting novelty, limitations, and methodological soundness. Users can interact with the paper through natural language queries to extract specific insights. This tool is especially valuable for researchers, R&D teams, academic professionals, and students who need to stay updated on literature without manually reading through dozens of papers. It streamlines research workflows and boosts analytical productivity across disciplines.

Key Features:

• Multi-section Summarization – Separately processes and summarizes each section of a research paper for clearer understanding.
• Reviewer-Style Analysis – Automatically generates comments on paper structure, contributions, and improvement areas.
• Interactive Q&A Interface – Enables users to ask detailed questions like “What algorithm is proposed in this study?”
• Support for Domain-Specific Prompts – Fine-tuned prompts for AI/ML, life sciences, economics, and more.

Ideal Use Cases or Scenarios:

• Academic researchers conducting rapid literature reviews
• PhD students preparing related work or research critiques
• Research labs screening new papers weekly
• EdTech platforms offering AI-based research assistants to learners

Deliverables:

• Backend pipeline for PDF ingestion, chunking, and LLM processing
• Streamlit-based front-end with upload, summary view, and Q&A interface
• Sample corpus of academic papers for demonstration
• Predefined templates for review generation and critique output
• Detailed documentation on architecture, prompt engineering, and customization options
• Optional integration with Arxiv, Semantic Scholar, etc. for fetching papers