Natural Language Processing for Text Summarization (Udemy.com)
Understand the basic theory and implement three algorithms step by step in Python! Implementations from scratch!
Created by: Jones Granatyr
Last updated April 2023
What you will 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
How it compares
Against the other 126 rated Udemy courses in Deep Learning on CourseDuck:
- Rating4.6 out of 5higher than 65% of them
- Reviews421#74 of 127 by number of reviews
- Length5 hoursshorter than 76% of them
- Last updatedApril 2023earlier than 70% of them
Course Description
The area of 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! Another important application is the automatic document summarization, which consists of generating text summaries. Suppose you need to read an article with 50 pages, however, you do not have enough time to read the full text. In that case, you can use a summary algorithm to generate a summary of this article. The size of this summary can be adjusted: you can transform 50 pages into only 20 pages that contain only the most important parts of the text!
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. During the lectures, we will implement each of these algorithms step by step using modern technologies, such as the Python programming language, the NLTK (Natural Language Toolkit) and spaCy libraries and Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.
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. At the end of the course, you will know everything you need to create your own summary algorithms! If you have never heard about text summarization, this course is for you! On the other hand, if you are already experienced, you can use this course to review the concepts.
Instructor Details
- 4.6 Rating
421 Reviews
Jones Granatyr
Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.
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Reviews
By Jani R on 12/28/2025
Before this course, I was already familiar with many basic NLP techniques, including the NLTK library, but I was not familiar with text summarization techniques. I found this course very useful for understanding how text extractive summarization techniques work andevolved and what other tools exist. I found the practical examples of implementing these classic techniques very useful since the preprocessing techniques are included even in the more advanced methods using BERT. One great feature of these classic methods is that they can be run on your own computer and that are much more energy efficient than running an large AI data center.
By Sarah Frota Alves on 1/28/2025
Curso muito bom para iniciantes, pois o professor explica muito bem o conteúdo passo a passo e fala um inglês simples de entender. Esse curso me ajudou tanto no conteúdo quanto na minha imersão de inglês.
By Nerian Willson on 8/17/2024
This course will be really beneficial to me because I am now working on my master's research in Natural Language Processing, specifically sentiment analysis, and I am quite interested in this topic.
By Litzy Guzman on 4/29/2024
Excellent course! The instructor is very easy to understand and knowledgeable about the material he is teaching. His delivery is extremely well-organized, making learning very simple.
By Sonia Rani on 11/2/2023
It is good course for beginner level but it contained only extractive summarization techniques ; It is not contained detailed techniques of Abstractive summarization. It can be more better if it also contains abstractive summarization with current strategies like transformers based methods.
By Anonymized User on 10/11/2023
I love the structure of the training. Presenting the basics first to build up the foundation of what is necessary for text summarization is an excellent approach. Then finally listing other libraries like Sumy and Bert gives the students more hints of what is out there. I know my next steps now to build my tool kits to extract text summarization from other sources . Thank you Professor Jones for an excellent presentation. Well done
By Balam Sinhué López Herrera on 3/16/2022
This course is a excellent introduction to text summarization, the classes are very friendly to beginners, the explanations are very clear and the code is great for understand only read how work the algoritms; amazing I am really happy with this course
By Elia Ibrahimi on 8/8/2021
What I always like about udemy courses is that they don't spent too much time in explaining theory, but they show you in practice and explain the theory behind it simultaneously.
By Angga on 7/26/2021
I want to know how to implement model to the web application, so other user can use this web applcation for Automatic Summarization.
By David C Edelstein on 6/14/2021
A good overview, but mostly a tutorial on how to use Python libraries. Actual discussion of the NLP algorithms was largely limited to providing links to papers. Good for learning about some available techniques, not a deep dive into text summarization.
Quality Score
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Overall Score : 92 / 100












