Problem: Businesses struggle to analyze large volumes of text quickly, leading to inefficiencies in decision-making.
Outcome: Develop an NLP-based model to automatically summarize long documents into key points.
Week 1-2: Data Collection & Preprocessing
Gather datasets (news articles, research papers).
Preprocess text (remove stop words, stemming, lemmatization).
Week 3-4: Exploratory Data Analysis (EDA)
Analyze word frequencies, TF-IDF scores.
Implement Named Entity Recognition (NER).
Week 5-6: Model Development (Extractive & Abstractive Summarization)
Train Transformer-based models (BERT, T5).
Implement TextRank for extractive summarization.
Week 7-8: Model Optimization & Performance Testing
Fine-tune models for better summarization quality.
Evaluate using ROUGE scores.
Week 9-10: API & UI Development
Develop REST API for text summarization.
Create a simple web-based summarization tool.
Week 11-12: Final Report & Deployment
Document research findings.
Deploy model and present to stakeholders.
