A text summarization model takes a long text and creates a shorter version while preserving its main points. It works by extracting key sentences directly (extractive summarization) or rephrasing the content into a shorter form (abstractive summarization). If you want to learn how to build a text summarization model using LLMs, this article is for you. In this article, I’ll take you through a guide to building a text summarization model using LLMs with Python.
Text Summarization Model using LLMs
To build a text summarization model, first, we need to choose a pre-trained language model like T5. Then, we need to tokenize the input text, which converts it into a format the model can process. The next step will be to use the model to generate a summary by specifying parameters like maximum length and beam search for better results. The final step will be to decode the generated tokens back into readable text and adjust parameters to improve the summary quality.
So, let’s start building a text summarization model using LLMs with Python step by step.
Step 1: Select a Suitable LLM
Choose a pre-trained model designed for text generation tasks. The T5 model (Text-to-Text Transfer Transformer) by Google is one such model that is effective for various text-based tasks like translation, question-answering, and summarization. You can learn about this model in detail here.
Step 2: Install Required Libraries
Install the transformers library by Hugging Face. It provides easy access to various pre-trained models and tokenizers. If you are using Google Colab, you will find it pre-installed in the Colab environment. To install it on your local machine, run the command mentioned below on your terminal or command prompt:
pip install transformers
Step 3: Load the Pre-trained Model and Tokenizer
Now, we need to import the T5Tokenizer and T5ForConditionalGeneration classes from the transformers library. Select a model like t5-small, t5-base, or t5-large based on the requirement and computational capacity:
from transformers import T5Tokenizer, T5ForConditionalGeneration # load pre-trained T5 model and tokenizer model_name = "t5-small" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name)
To select between t5-small, t5-base, or t5-large, consider your computational resources and accuracy needs. t5-small is faster and requires less memory, which makes it suitable for quick tasks or limited hardware. t5-base offers a balance between speed and performance, ideal for general use. t5-large provides the highest accuracy but needs more memory and processing power, which makes it better for scenarios where performance is more important than speed.
Step 4: Prepare the Text for Summarization
Next, we need to define the text that needs to be summarized. The text should be prefixed with the keyword “summarize:” for the T5 model to recognize the task properly:
text = """ The COVID-19 pandemic has brought unprecedented challenges to the global economy... """ # prepare the text for the T5 model by adding the "summarize:" prefix input_text = "summarize: " + text
The “summarize:” prefix for the T5 model is necessary because T5 is a “text-to-text” model that needs to understand what task it should perform (e.g., summarization, translation, or question-answering). The prefix helps the model identify that it should generate a summary of the input text. Without this instruction, the model might not produce the intended summarization output.
Step 5: Tokenize the Input Text
The next step is tokenization. Tokenization is the process of converting text into a sequence of integers that represent the model’s vocabulary. The max_length parameter helps manage large texts by truncating or limiting the input size:
# tokenize the input text
input_ids = tokenizer.encode(input_text,
return_tensors="pt",
max_length=512,
truncation=True)Step 6: Generate the Summary
Now, use the generate method to produce the summary. Important parameters include:
- max_length: The maximum number of tokens in the output.
- num_beams: The number of beams for beam search (higher values improve results but increase computation).
- length_penalty: Adjusts the length of the summary (penalizes lengthy outputs).
# generate the summary
summary_ids = model.generate(
input_ids,
max_length=50, # maximum length of the summary
num_beams=4, # beam search for better results
length_penalty=2.0, # length penalty to avoid lengthy summaries
early_stopping=True
)Step 7: Decode and Display the Summary
The final step is to decode the generated summary using the tokenizer to convert the tokens back to readable text:
# decode the summary
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("Summary:", summary)Summary: the pandemic has brought unprecedented challenges to the global economy. governments have implemented strict lockdowns and social distancing measures. many industries have been severely affected, leading to widespread job losses and financial instability.
By following this step-by-step approach, you can build and customize a text summarization model using LLMs like T5 with Python.
Summary
So, to build a text summarization model, first, we need to choose a pre-trained language model like T5. Then, we need to tokenize the input text, which converts it into a format the model can process. The next step will be to use the model to generate a summary by specifying parameters like maximum length and beam search for better results. The final step will be to decode the generated tokens back into readable text and adjust parameters to improve the summary quality.
I hope you liked this article on building a text summarization model using LLMs with Python. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
