A Guide to Building an AI Text Summarizer Model Using Python

You may have used a text summarizing tool at least once in your life. It is a facility through which you can quickly and efficiently condense lengthy text into a concise and precise summary. 

But as a developer, have you ever wondered how exactly such utilities are built? If so, then the answer is – these can be built using advanced programming languages like Python. Python is a well-known high-level language that is widely used for developing tools, websites, and applications. 

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

Steps to Develop an AI Summarizing Tool Using Python Language

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

1. Decide on the Type of Summarizing Model

First of all, you are required to decide what type of text summarizer model you want to build. 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 generate an output summary. 
• Abstractive model – This one has the opposite working. Based on your given text, it will not only create a summary but also use new and improved words that the source content does not contain. 

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

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

2. Set Up the Environment

To get started, create a virtual environment to proceed with the 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 privileges. Now, it is time to change the directory where you are planning to save the model files. 

This is the code you need to enter: 

Python -m venv text_summarization
text_summarization\Scripts\activate

After entering, press the “Enter” key, and your virtual environment will be created. 

3. Collect the Dataset

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

Alternatively, you can also use the Hugging Face dataset library, which contains all the required data, eliminating the need for you to gather it on your own. 

4. Install the Required Libraries

You are required to download and install multiple Python libraries to build an AI text summarizer model. You need transformers, NLTK, Torch, sentencepiece, rouge-score, and more. Refer to Python’s official website for downloading these libraries. 

When done, use the following code to begin the installation process: 

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

Do not forget to install the dataset if you are using Hugging Face. 

pip install datasets

On the other hand, if you are relying on your own data collection, then you have to manually import it using the code below. 

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

5. Import Dependencies

Now, it is time to create a new Python file, e.g., summarizer.py, to ultimately start importing the required modules. 

from transformers import pipelineimport nltkimport torch

It is also suggested to download the necessary tokenizers, if required: 

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

6. Choosing & Loading a Pre-trained Abstractive Summarization Model

In this step, you have to pick an abstractive summarization model that will make your model work. There are many popular options available that you can go with: 

• Bart – specifically useful for summarization and other NLP tasks
• T5 – Ideal for Google-based data
• Pegasus – Useful for Google and optimized for concise summaries

For this guide, we will be using T5; here is the code you will need for loading. 

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

7. Create a Summarization Function

When the model is loaded, you then have to define a Python function that will allow the model to quickly and efficiently summarize the given text. 

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 like BART and T5 have a token input limit (usually 1024 tokens). So, in case your text is longer than this limit, then you definitely have to break it down into smaller chunks and summarize them individually. 

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

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 Summarizer Model

Finally, it is now time to test your model to determine whether it is efficiently summarizing the given text or not.

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

Enter your text in the specified place and run the script to see the summarized output. 

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

A Real-World Example of Python-based AI Text Summarizer

The internet is filled with a wide range of AI-backed text summarizing tools. One of them includes the AI Summarizer - a Python-based text summarizer that uses advanced algorithms to quickly and accurately condense the given text into a precise and concise summary. 

Take a look at the screenshot below as a reference. 

Source: https://www.summarizer.org/

So, by following the aforementioned approach and then spending time and effort on creating a good UI, you can also come up with a model like AI Summarizer. 

Conclusion

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

In this blog post, we have discussed a step-by-step procedure for building such a text summarizing model using Python. We hope that you will find this blog valuable and interesting! 

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