How to Perform Text Summarization using Transformers in Python

How to Perform Text Summarization using Transformers in Python

Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python. We will use HuggingFace's transformers library in Python to perform abstractive text summarization on any text we want.

Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python.

Text summarization is the task of shortening long pieces of text into a concise summary that preserves key information content and overall meaning.

There are two different approaches that are widely used for text summarization:

  • Extractive Summarization: This is where the model identifies the important sentences and phrases from the original text and only outputs those.
  • Abstractive Summarization: The model produces a completely different text that is shorter than the original, it generates new sentences in a new form, just like humans do. In this tutorial, we will use transformers for this approach.

In this tutorial, we will use HuggingFace's transformers library in Python to perform abstractive text summarization on any text we want.

The reason why we chose HuggingFace's Transformers as it provides us with thousands of pretrained models not just for text summarization, but for a wide variety of NLP tasks, such as text classification, question answering, machine translation, text generation and more.

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