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How to Train An AI Text Summarization Model Using Python

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Updated on
Mar 23, 20246 min read
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    Table of content

  • What is An AI Text Summarization Model?
  • arrow
  • Different Steps to Train An AI Text Summarization Model Using Python
  • Real-Time Working Demonstration of An AI Summarization Model Trained Using Python
  • Conclusion

To provide straightforward and information-rich content, the people including writers, marketers, and students condense their writing into a possible short form. To perform this condensation AI summarization is utilized. Text summarization is done by online AI-based text summarizing tools.

The models of AI text summarization tools are based on artificial intelligence. 

However, these models are mostly trained with the help of Python. Python is a high-level and simple programming language that is utilized for the analysis and building of online apps or websites. 

Nowadays, this language is widely used to train the AI models of different types of tools, including AI text summarizers.

In this article, we will write some beginner-friendly steps to reveal the whole process of how to train an AI text summarization model using Python.

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What is An AI Text Summarization Model?

AI text summarization models are based on artificial intelligence that functions to summarize, condense, or shorten long-form writings. 

To do so, they extract the most important parts of a provided text and organize them into a text form. However, their working mechanism does not cause any harm to the actual meaning of a text.

The AI text summarization models are mostly trained in the business of Python. Before we delve into the actual process of how these models are trained, let us understand two primary types of text summarization:

Two Primary Types Of Summarizations

1. Extractive Summarization: This type of text summarization refers to the process of identifying the most important or key points/sentences within the original text. 

And collecting those pivotal sentences together to form a summary. In other words, it is picking the “best bits" from actual writing. The Python libraries that are utilized for this approach include Sumy and Gensim.

2. Abstractive Summarization: This summarization technique involves the apprehension of the intent meaning of writing and generating a concise summary utilizing new words, phrases, and sentences. 

The libraries that are involved in the training process of this type of summarization model include TensorFlow and PyTorch.

Different Steps to Train An AI Text Summarization Model Using Python

To train an AI summarization model using Python, we divided the whole procedure into different steps that are below.

1. Install the Python and Libraries

Training an AI text summarization model requires Python and some libraries on your device (computer or laptop). To download and Install Python on your device, you should go to its official website. 

Once you have installed Python, you will need to install some necessary libraries. We listed these libraries in the below points:

  • Gensim.
  • NLTK.
  • BeautifulSoup.

To install the libraries mentioned above, you should open your terminal and execute the below-given command:

2. Collect Data

The second step involves collecting your data. This step is essential, as data is the backbone of any machine learning or training project. For the text summarization model, you must collect data from multiple sources and documents and their corresponding summaries. 

For Example, You can collect the following type of data:

  • Scientific papers with their abstracts.
  • Books with their summaries.
  • News articles along with their summaries
  • Articles and blogs along with their conclusion and introductions (that are also summaries).

Moreover, you can create a personalized set of data by creating some writing with their summaries. Additionally, there are many online datasets available on the internet that are helpful to accomplish the data collection step. These include CNN/Daily Mail, PubMed, etc.

3. Preprocessing Data for Training

After collecting your data, it needs to be pre-processed to train your text summarization model. Let us prepare your data in the below sections:

You should clean the collected text or data by removing all the following things:

  • Stop words.
  • Punctuation marks.
  • Noise from writing.

You should tokenize the data into sentences and phrases. For this, you can utilize techniques such as stemming or lemmatization. The former is used to process words to their stem words and the latter to break words down to their root ones.

Additionally, you can use a library i.e. NLTK for this purpose. For example: we pre-processed data with this library. 

The below image highlights our act:

4. Load a Suitable Model Architecture

This step involves the selection and loading of a model architecture that corresponds to the type of your chosen AI Text summarization. To continue this process, we supposed an extractive type of summarization.

For this, we loaded a statistical model named TextRank on Python. It is a graph-based model that ranks the sentences according to their value/significance.

The below image highlights the working of TextRank with Python:

5. Train the Model and Make Continuous Improvements

Now is the time to feed the pre-processed data into the model architecture and let it learn to recognize the key information to generate summaries. You should provide multiple types of inputs to the model to train it for multiple niche writings
 

Practical Demo:

Moreover, we suggest monitoring your model’s accuracy along the way. To do so, you should evaluate your trained model on a separate dataset to test its performance.
 

For an abstractive type of summarization, you should use metrics like the ROUGE score. But, for extractive summarization, you must use the BLEU score metrics. Based on the score given by these models, you can train the model for further improvements, accuracy, and fluency. 

Note: Remember, the AI journey involves continuous learning and improvements along with innovations and advancements.

 

 

Real-Time Working Demonstration of An AI Summarization Model Trained Using Python

Nowadays, multiple online AI text summarization models have been trained using Python. To demonstrate real-time working, we picked an AI summarizer that we observed was trained by using Python.

Analyzing this real-time work will help you modify and improve your own AI text summarization model.

In the below sections, we summarized a text using the AI text summarization model of the summarizer as mentioned earlier
 

Sample Text:

“Our world is at the end of chaos due to a high increase in pollution and climate changes. We can save this by adopting some environmentally friendly practices. We are all responsible for elevating and bettering the current situation of our globe.”

Into

AI Summarized:

“Our world is on the brink of chaos due to excessive pollution and climate change.”


 

Image of Practical Use:

Conclusion

The use of AI text summarization models is tremendously increasing in the field of writing. These models are mostly trained with the help of Python, which is a simple programming language. 

In the above post, we covered different simple-to-implement steps to train an AI text summarization model using Python. Following the above steps with their practical usage examples, you can easily do training on your model.

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