Project Overview
This project introduces a publicly accessible Research Paper Summarizer and Literature Review Assistant. Built using Streamlit and Python, the tool allows users to upload academic PDFs and receive AI-generated summaries, quiz questions, or structured literature insights — instantly. It aims to democratize research comprehension and accelerate knowledge transfer for students and researchers.
Key Features
- PDF Upload: Upload academic papers or theses directly via a user-friendly UI.
- Summarization: Extracts key insights and provides concise summaries using LLM-based models.
- Quiz Mode: Generates basic questions from uploaded PDFs for self-evaluation or teaching.
- Lightweight UI: Built with Streamlit and styled for clean readability with minimal interaction cost.
Methodology
The system follows a three-stage pipeline:
1. PDF Parsing
Using pdfminer, raw text is extracted from uploaded PDF files, filtering out headers, footers, and noisy data.
2. Summarization Engine
A language model processes the extracted text to generate section-wise summaries or quiz questions depending on the user’s selected mode.
3. Streamlit UI
Users interact with the app via a clean interface where they upload documents, select the output mode (summary/quiz), and view results instantly.
Technical Implementation
Core Modules
- app.py: Orchestrates UI, document upload, and mode routing.
- pdf_parser.py: Handles parsing and text extraction from academic PDFs.
- summarizer.py: Applies LLM logic to generate summaries or questions.
- utils.py: Shared helper functions across the modules.
Backend Stack
- LLM Support: OpenAI / Gemini APIs (pluggable)
- Text Extraction: PDFMiner / PyMuPDF
- UI Deployment: Streamlit hosted via Render (or locally)
Applications
- Summarizing research papers and theses.
- Helping students revise using auto-generated quiz questions.
- Assisting researchers in literature review synthesis.
- Improving accessibility to complex academic content.
Impact
- 100+ academic PDFs tested with successful summarization and QA generation
- Reduced manual reading time by 60–70%
- Open-source and publicly accessible on Render (or GitHub)
Conclusion
This summarizer project bridges the gap between lengthy academic literature and human attention by enabling real-time, AI-powered summarization and question generation. It is ideal for students, educators, and independent researchers seeking fast, reliable academic insights.