Study on DocSnap: Intelligent Summarization for Fast Decision-Making

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.