Natural Language Processing for Text Summarization

Natural Language Processing

Natural Language Processing for Text Summarization

Understand the basic theory and implement three algorithms step by step in Python! Implementations from scratch!

What you’ll learn 

  • Understand the theory and mathematical calculations of text summarization algorithms
  • Implement the following summarization algorithms step by step in Python: frequency-based, distance-based and the classic Luhn algorithm
  • Use the following libraries for text summarization: sumy, pysummarization and BERT summarizer
  • Summarize articles extracted from web pages and feeds
  • Use the NLTK and spaCy libraries and Google Colab for your natural language processing implementations
  • Create HTML visualizations for the presentation of the summaries


  • Programming logic
  • Basic Python programming


The area of ​​Natural Language Processing – PLN (Natural Language Processing – NLP) is a subarea of ​​Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others!

Based on this, this course presents the theory and mainly the practical implementation of three text summarization algorithms: (i) frequency-based, (ii) distance-based (cosine similarity with Pagerank) and (iii) the famous and classic Luhn algorithm, which was one of the first efforts in this area.

In addition to implementing the algorithms, you will also learn how to extract news from blogs and the feeds, as well as generate interesting views of the summaries using HTML! After implementing the algorithms from scratch, you have an additional module in which you can use specific libraries to summarize documents, such as: sumy, pysummarization and BERT summarizer.

Who this course is for:

  • People interested in natural language processing and text summarization
  • People interested in the spaCy and NLTK libraries
  • Students who are studying subjects related to Artificial Intelligence
  • Data Scientists who want to increase their knowledge in natural language processing
  • Professionals interested in developing text summarization solutions
  • Beginners who are starting to learn natural language processing

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