Text Summarization

Text summarization is the art of condensing large amounts of information into a shorter, meaningful version while preserving the essential ideas.

Whether summarizing a research paper, legal document, news article, or meeting transcript, modern NLP models can perform this task with remarkable fluency. But behind this seemingly simple task lies a rich ecosystem of methods, models, and design choices.

In this lesson, you’ll understand how summarization actually works, why transformers drastically changed the field, how modern models like Flan-T5, BART, Pegasus, and Mistral create summaries, and how to use the Hugging Face summarization pipeline effectively.

What is text summarization?

Text summarization is the process of condensing text while retaining its key meaning.

Traditionally, approaches fell into two categories: extractive and abstractive, but the arrival of transformer-based models brought a more sophisticated and human-like way of condensing information. Summarization facilitates faster decision-making and comprehension, particularly when working with lengthy content such as research papers, emails, or customer reviews.

Today, the task powers everything from news digests to meeting minutes generators.

Extractive vs. abstractive summarization

Understanding these two fundamental approaches is essential before exploring modern transformer models.

• Extractive summarization works by selecting the most important sentences or phrases from the text and arranging them into a shorter version. It does not rewrite or paraphrase; it simply picks the best bits.

• Abstractive summarization, however, rephrases, reorganizes, and often rewrites content in a more human-like way. It produces sentences not present in the original text and captures the text’s meaning rather than its exact wording.

Below is a comparison:

Extractive models are simple and safe, but they are limited. Abstractive models offer more natural summaries but require more computational power and robust training.