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Text Summarization using HuggingFace Model

Last Updated : 23 Jul, 2025

Text summarization involves reducing a document to its most essential content. The aim is to generate summaries that are concise and retain the original meaning. Summarization plays an important role in many real-world applications such as digesting long articles, summarizing legal contracts, highlights from research papers, etc.

With the use of deep learning and pre-trained language models, summarization systems have become more accurate and context-aware. Hugging Face Transformers library provides easy access to powerful summarization models like T5. In this article, we explore how to implement a text summarizer using the T5 model and deploy it through an interactive interface using Gradio.

Text summarization techniques fall into two primary categories:

• Extractive Summarization: In this approach, important phrases are extracted directly from the input text to form the summary. It relies on ranking the most informative parts of the document but does not rephrase the content.
• Abstractive Summarization: This method attempts to understand the text’s meaning and generate entirely new sentences that convey the same information. It mimics the way humans summarize and often provides more coherent and natural results.

Abstractive summarization is more complex but also more flexible. It requires a model that understands grammar, context and can generate fluent language, all of which are made possible with transformer-based architectures.

Hugging Face’s transformers library has provided access to cutting-edge NLP models. The T5 (Text-to-Text Transfer Transformer) model is particularly well-suited for summarization. It reframes every NLP task as a text generation problem. For summarization, the model simply receives input text prefixed with a task keyword and outputs the summary.

Variants like t5-small, t5-base and t5-large offer flexibility in balancing speed and accuracy. T5 models are pre-trained on a mixture of supervised and unsupervised tasks making them general-purpose and robust across domains.

Install the necessary packages:

• transformers – For loading pre-trained models like T5.
• torch – Backend framework to run the model.
• gradio – To build a simple web interface.

!pip install transformers
!pip install torch
!pip install gradio
!pip install datasets

These libraries handle model inference, text preprocessing and building a simple web interface.

Here we load our T5 model and tokenizer converts text into token IDs and the model generates summaries from these encodings.

from transformers import T5Tokenizer, T5ForConditionalGeneration

model_name = 't5-small'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

Output:

This function handles preprocessing, testing and postprocessing in a single step. Beam search is used to improve summary quality.

def summarize(text):
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

This code creates an interactive interface for summarization. Users can paste any long paragraph and instantly receive a condensed summary.

import gradio as gr

iface = gr.Interface(
    fn=summarize,
    inputs="text",
    outputs="text",
    title="Text Summarization with T5",
    description="Enter text to get a summarized version using the T5 model."
)

iface.launch()

Output:

• Text-to-Text Flexibility: T5’s uniform approach makes it easy to apply to summarization and other tasks without architectural changes.
• Pretraining and Fine-Tuning: T5 is pretrained on large datasets and can be fine-tuned for domain-specific summarization.
• Multilingual Support: It can be extended to summarize texts in multiple languages with the right data.

• Input Size Limit: T5 has a maximum input length (1024 tokens for t5-small), so long texts must be truncated.
• Computational Cost: Larger models like t5-large provide better performance but require more memory and time.
• Bias and Hallucination: Like all large language models, T5 may sometimes generate inaccurate or biased summaries.

As transformer models continue to improve, summarization systems will become even more fluent and aligned with human expectations. Whether summarizing legal documents or social media threads, models like T5 offer a reliable and scalable solution.

You can download source code from here.

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