What is Text Summarization?
Text summarization is the process of shortening a document while preserving its main ideas. There are two main types:
Extractive Summarization: Selects important sentences from the original text.
Abstractive Summarization: Generates new sentences that capture the meaning of the original text (more like how humans summarize).
Deep learning is primarily used for abstractive summarization.
🔧 Step-by-Step: Building a Text Summarization System
✅ Step 1: Define the Problem
Decide:
Will the summaries be extractive or abstractive?
What type of content are you summarizing (news articles, reviews, long documents, etc.)?
📁 Step 2: Prepare the Dataset
Popular datasets for summarization:
CNN/DailyMail
XSum (BBC articles with single-sentence summaries)
Gigaword
Newsroom
Amazon/Rotten Tomatoes reviews (for review summarization)
You can also create a custom dataset with text-summary pairs.
Example:
{
"article": "The president met with the foreign minister...",
"summary": "The president met a foreign official."
}
🧹 Step 3: Preprocess the Data
Typical NLP preprocessing:
Lowercasing
Removing special characters
Tokenization (handled automatically by transformers)
Limiting input/output lengths (e.g., 512 tokens max)
🧠 Step 4: Choose a Deep Learning Model
For abstractive summarization, transformer models are state-of-the-art:
🔥 Pretrained Models:
T5 (Text-to-Text Transfer Transformer)
BART (Facebook)
PEGASUS (Google)
GPT-3.5/4 (OpenAI) – via API
mT5 – for multilingual summarization
🛠️ Step 5: Fine-tune a Pretrained Model
Using Hugging Face Transformers:
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")
# Add prefix for task type
input_text = "summarize: " + article
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
# Generate summary
summary_ids = model.generate(input_ids, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
✅ Replace "t5-small" with "t5-base" or "facebook/bart-large-cnn" for better results.
📈 Step 6: Train on Custom Dataset (Optional)
If you have a domain-specific dataset:
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
training_args = Seq2SeqTrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=4,
num_train_epochs=3,
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
tokenizer=tokenizer,
)
trainer.train()
📊 Step 7: Evaluate the Model
Use metrics such as:
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) – most common
BLEU – borrowed from machine translation
BERTScore – semantic similarity
from datasets import load_metric
rouge = load_metric("rouge")
results = rouge.compute(predictions=pred_summaries, references=ref_summaries)
🚀 Step 8: Deploy the Summarization System
Use Flask, FastAPI, or Streamlit for APIs or UIs.
Convert the model to ONNX or TorchScript for performance optimization.
Optionally deploy on cloud (AWS, GCP, Azure) or use Hugging Face Spaces.
✅ Tips for Better Performance
Use beam search or top-k sampling for more fluent summaries.
Fine-tune on domain-specific data (e.g., legal, medical).
Limit summary length appropriately for your use case.
Clean noisy training data to avoid garbage outputs.
📚 Useful Tools and Libraries
Tool Use
Hugging Face Transformers Pretrained models & training
Datasets (Hugging Face) Summarization datasets
TensorFlow / PyTorch Deep learning backends
ROUGE / NLTK Evaluation metrics
Streamlit / Gradio UI for demos
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