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.