A Guide to Creating an AI Text Summarization Model Using Python

You’ve probably used a text summarization tool at least once in your life. This is a facility through which you can quickly and efficiently condense long texts into concise and precise summaries.

But as a developer, have you ever wondered how these utilities are actually created? If yes, then the answer is – it can be created using advanced ones programming language like Python. Python is a well-known high-level language that is widely used to develop tools, websites, and applications.

In this detailed blog post, we will explain how you can use Python to develop AI-powered text summarization models.

Steps to Develop an AI Summarization Tool Using Python Language

Here is the step-by-step procedure you need to follow to create a custom AI text summarization model using Python.

1. Determine the Summarization Model Type

First of all, you are required to decide what type of text summarization model you want to create. You have two options to choose from:

• Extractive model – This tool will work by using the same words and phrases in the input text to produce an output summary.
• Abstractive model – This one is just the opposite. Based on the text you provide, it will not only create a summary but also use new and better words that are not present in the source content.

On the internet, you will mostly find AI-powered abstract text summarization tools. This is because they not only condense the text but also improve its overall quality.

Therefore, in this guide, we will build an abstractive summarization model.

2. Set the Environment

To get started, create a virtual environment to continue development. This keeps your project environment isolated from the system environment, reducing the risk of package conflicts.

So, open Command Prompt on your computer with administrative rights. Now, it’s time to change the directory where you plan to save the model files.

This is the code you need to enter:



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Python -m venv text_summarization
text_summarization\Scripts\activate

After logging in, press the “Entering” key, and your virtual environment will be created.

3. Collect Data Sets

If your goal is to fine-tune the model to improve the overall summarization process for a specific domain, such as large text. Then, it is important to collect a data set. You can choose online blogs, research papers, journals, essays, business proposals, etc., to get data and then save them in CSV format files.

Alternatively, you can also use the Hugging Face dataset library, which contains all the necessary data, so you don’t have to collect it yourself.

4. Install Required Libraries

You are required to download and install some Python libraries to build an AI text summarization model. You need transformer, NLTK, Torch, sentence, rouge-score and more. Look Python official website to download this library.

Once finished, use the following code to start the installation process:

pip install transformerspip install torchpip install nltkpip install sentencepiecepip install rouge-score

Don’t forget to install the dataset if you use Hugging Face.

pip install dataset

On the other hand, if you rely on collecting your own data, you will have to import it manually using the code below.



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from datasets import load_dataset
# Load a dataset like CNN/DailyMaildataset = load_dataset("cnn_dailymail", "3.0.0")print(dataset['train'][0])

5. Import Dependence

Now, it’s time to create a new Python file, for example, summaryr.py, to start importing the required modules.



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from transformers import pipelineimport nltkimport torch

It is also recommended to download the required tokenizer, if needed:



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nltk.download('punkt')  # for sentence tokenization

6. Select & Load a pre-trained Abstractive Summarization Model

In this step, you must choose an abstractive summarization model that will make your model work. There are many popular options available that you can use:

• Bart – especially useful for summarization and other NLP tasks
• T5 – Ideal for Google based data
• Pegasus – Useful for Google and optimized for short summaries

For this guide, we will use T5; this is the code you need to load.



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summarizer = pipeline("summarization", model="T5")

7. Create a Summarization Function

When the model is loaded, you must define a Python function that allows the model to quickly and efficiently summarize the given text.



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def summarize_text(text):    # Adjust the length parameters as needed    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)    return summary[0]['summary_text']

8. Handle Large Text (Optional but Important)

Please note that models such as BART and T5 have a token input limit (usually 1024 tokens). So, if your text is longer than this limit, you should break it into smaller parts and summarize them one by one.

For this purpose, you can use the following Python code.



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from nltk.tokenize import sent_tokenize
def split_into_chunks(text, max_tokens=1000):    sentences = sent_tokenize(text)    chunks = []    chunk = ""    for sentence in sentences:        if len(chunk) + len(sentence) <= max_tokens:            chunk += " " + sentence        Else:            chunks.append(chunk)            chunk = sentence    chunks.append(chunk)    return chunks
def summarize_long_text(text):    chunks = split_into_chunks(text)    summaries = [summarizer(chunk, max_length=130, min_length=30, do_sample=False)[0]['summary_text'] for chunk in chunks]    return " ".join(summaries)

9. Test Your Text Summarization Model

Finally, it is time to test your model to determine whether it summarizes a particular text efficiently or not.



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if __name__ == "__main__":    input_text = """    Enter Your Text Here    """    print("Summary:\n", summarize_long_text(input_text))

Enter your text in the specified space and run the script to see the summary results.

So, this is the proven approach you need to follow to build an AI-powered text summarization tool.

Real World Example of Python Based AI Text Summarization

The internet is filled with various AI-powered text summarization tools. One of them includes AI Summarizer – a Python-based text summarizer that uses advanced algorithms to quickly and accurately condense specific text into a precise and concise summary.

Take a look at the screenshot below for reference.



Source:

So, by following the above-mentioned approach and spending time and effort on creating a good UI, you too can produce models like AI Summarizer.

Conclusion

Python is a high-level programming language that is widely used to build web tools and software, such as AI-based text summarizers. It works by condensing long content into a precise and concise summary without sacrificing quality and meaning.

In this blog post, we have discussed a step-by-step procedure to create a text summarization model using Python. We hope you find this blog useful and interesting!

FAQs

The model is extractive and abstractive. Extractive summarization models produce summaries using the same words and phrases as those contained in the input text. On the other hand, abstractive models work by using new words to create a summary version.

Python offers various AI-powered libraries, such as NLTK, Hugging Face, and Transformers, for developing and training summarization models.

Yes, you can rely on pre-trained models like BART, T5 and others to create summary models.