Cleaning text is an important step in natural language processing (NLP) as it helps to improve the performance and accuracy of NLP models. It also makes the text data more consistent and easier to work with.

Using regular expressions (`re`) is a common way to clean text data because it allows for precise and flexible manipulation of the text.

Cleaning text can involve a variety of tasks, such as:

- Removing irrelevant characters, such as punctuation marks, special characters, and digits;
- Removing extra white spaces and line breaks;
- Removing stop words (common words such as `'the'`, `'and'`, `'a'`, etc.);
- Lowercasing all text.

 In this project, we are going to create a text summarizer using Python.