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Python Text Summarizer: Condensing Content with Code

Text CleaningText Cleaning

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.


  1. Replace any single digit character from 0 to 9 with a white space;
  2. Replace any whitespace character with a white space;
  3. Replace any single symbol with a white space;
  4. Replace any single digit character with a white space.

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Section 1. Chapter 3

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