Text Summarizer

Text Summarizer – IT and Computer Engineering Guide

1. Project Overview

Objective: Develop a system capable of summarizing long text documents using Natural Language Processing (NLP) and Transformer-based models like BERT, GPT, or T5.
Scope: Applications include summarizing articles, legal documents, research papers, and news reports.

2. Prerequisites

Knowledge: Familiarity with Python programming, NLP concepts, and Transformer models.
Tools: Python, Hugging Face Transformers library, PyTorch or TensorFlow, and NLP preprocessing libraries like NLTK or SpaCy.
Data: Text datasets for summarization (e.g., CNN/DailyMail dataset or custom text data).

3. Project Workflow

- Data Collection: Obtain and preprocess text data for summarization.

- Model Selection: Choose a pre-trained Transformer model such as BERTSUM, T5, or Pegasus.

- Fine-tuning (optional): Train the model on domain-specific data if required.

- Inference: Generate summaries from input text.

- Evaluation: Use metrics like ROUGE to assess summarization quality.

4. Technical Implementation

Step 1: Install Dependencies


# Install required libraries
!pip install transformers torch datasets nltk

Step 2: Load Pre-trained Model


from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load pre-trained model and tokenizer
model_name = "t5-small"  # Replace with your chosen model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Step 3: Preprocess Input Text


# Define the input text
input_text = "Your long text here..."

# Tokenize and prepare input
inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)

Step 4: Generate Summary


# Generate summary
summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

# Decode and print summary
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("Summary:", summary)

Step 5: Evaluate the Model


# Use ROUGE metrics for evaluation
from datasets import load_metric

rouge = load_metric("rouge")
results = rouge.compute(predictions=[summary], references=["Reference summary here"])
print(results)

5. Results and Insights

Analyze the quality of the generated summaries and compare them against reference summaries using metrics like ROUGE.

6. Challenges and Mitigation

Long Input Texts: Use chunking to process long documents in parts.
Relevance of Summaries: Fine-tune the model on domain-specific data if required.

7. Future Enhancements

Incorporate abstractive summarization techniques for more human-like summaries.
Deploy the model as a web application for end-user accessibility.

8. Conclusion

The Text Summarizer project demonstrates the power of Transformer models in NLP tasks, providing concise and accurate summaries for lengthy documents.