After Tokinzation?!

[Hamdysaad20]

After tokenization, you can use a variety of machine learning algorithms to create a model for text summarization. Some of the most common algorithms include:

Seq2Seq:

This is a neural network architecture that can be used to learn the relationship between an input sequence and an output sequence. In the case of text summarization, the input sequence would be the original text and the output sequence would be the summary.

Transformer:

This is another neural network architecture that is well-suited for text summarization. The transformer architecture does not rely on recurrent connections, which makes it faster and more efficient than seq2seq models.

Bart:

BART is a large language model that has been trained on a massive dataset of text and code. BART can be used for a variety of natural language processing tasks, including text summarization.

Once you have chosen an algorithm, you need to train your model on a dataset of text and summaries. The dataset should be large and diverse, so that your model can learn to generate summaries that are relevant and informative.

• After your model is trained, you can use it to generate summaries of new text. The quality of the summaries will depend on the quality of the training data and the complexity of the text.

Here are some additional tips for creating a good text summarization model:

• Use a large and diverse dataset of text and summaries.
• Use a powerful machine learning algorithm, such as seq2seq or transformer.
• Train your model on a powerful computing platform.
• Evaluate the quality of your summaries and make adjustments to your model as needed.
• With careful planning and execution, you can create a text summarization model that can generate summaries that are relevant, informative, and engaging.

[Hamdysaad20]

! important

thanks to @salah-mo , we should follow the extractive way so this steps will not work