This project is an AI-based tool that extracts and summarizes key sections—Introduction, Methodology, Results, and Conclusion—from academic research papers using state-of-the-art transformer-based NLP models. It helps researchers save time and enhance productivity by providing quick and coherent summaries of long academic texts.
- 🔍 Automatically identifies and extracts major research paper sections.
- 🤖 Summarizes each section using pre-trained Transformer models like Pegasus, T5, or GPT.
- 📊 Evaluates summary quality using ROUGE, BLEU, and BERTScore metrics.
- 📁 Accepts PDF or plain-text academic papers.
- 🌐 Easy-to-use, modular code with evaluation tools.
- Python 🐍
- Hugging Face Transformers 🤗
- Pegasus / GPT models
- NLTK & spaCy for preprocessing
- Evaluate library for scoring (ROUGE, BLEU, BERTScore)
- PyMuPDF or PDFMiner (for PDF parsing)