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.
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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.
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.
What's inside
Learning objectives
- 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
- 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
Syllabus
Introduction
Course content
Introduction to natural language processing
Source code and slides
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Reviews summary
Practical nlp for text summarization: algorithms & libraries
Regularly updated with modern NLP techniques like BERT.
Instructor provides clear explanations, suitable for beginners.
Strong emphasis on practical implementation and coding.
Activities
Review 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
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Supplement your course learning with insights from experts in the field, broadening your understanding of NLP concepts.
View
Natural Language Processing with Python:...
on Amazon
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-
Read selected chapters or sections of the book
-
Summarize key concepts and techniques
Create a curated list of NLP resources
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Support your learning and future endeavors by compiling a valuable collection of NLP-related resources.
Browse courses on
Resources
Show steps
-
Identify and gather high-quality resources such as articles, tutorials, libraries, and datasets
-
Organize the resources into a structured and easily accessible format
-
Share your compilation with the community through a blog post, GitHub repository, or other platform
Review fundamentals of natural language processing
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Starting this course with foundational knowledge will enable a stronger understanding of course materials and provide a more solid base for learning more advanced concepts.
Browse courses on
Natural Language Processing (NLP)
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-
Review concepts of syntax and semantics
-
Practice extracting named entities using Python libraries like spaCy and NLTK
-
Locate a natural language processing library like NLTK and explore its functionality
Four other activities
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Implement the Luhn algorithm in Python
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After learning about the famous Luhn algorithm, implementing it will solidify your understanding and strengthen your Python programming skills.
Show steps
-
Locate a Python implementation of the Luhn algorithm
-
Replicate the implementation yourself, step by step
-
Test your implementation on various text samples
Build a text summarization tool using Python
Show steps
Develop your skills across the board by using the concepts learned in this course to create a practical and tangible tool.
Browse courses on
Text Summarization
Show steps
-
Design the architecture of your tool, considering the different summarization algorithms covered in the course
-
Implement the tool using Python, integrating the chosen algorithms
-
Test and refine your tool on a variety of text samples to ensure its accuracy and efficiency
Contribute to open-source NLP projects
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Engage with the broader NLP community by contributing to open-source projects, expanding your knowledge and making a tangible impact.
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Open Source
Show steps
-
Identify open-source NLP projects that align with your interests
-
Explore the project's codebase and documentation
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
Participate in an NLP hackathon or competition
Show steps
Challenge yourself in a practical and competitive setting, applying your NLP skills to solve real-world problems.
Show steps
-
Find an NLP hackathon or competition that aligns with your interests
-
Form a team or work individually on developing a solution
-
Submit your solution and compete for recognition and prizes
Review 'Natural Language Processing with Python' by Steven Bird, Ewan Klein, and Edward Loper
Show steps
Supplement your course learning with insights from experts in the field, broadening your understanding of NLP concepts.
View
Natural Language Processing with Python:...
on Amazon
Show steps
- Read selected chapters or sections of the book
- Summarize key concepts and techniques
Create a curated list of NLP resources
Show steps
Support your learning and future endeavors by compiling a valuable collection of NLP-related resources.
Browse courses on
Resources
Show steps
- Identify and gather high-quality resources such as articles, tutorials, libraries, and datasets
- Organize the resources into a structured and easily accessible format
- Share your compilation with the community through a blog post, GitHub repository, or other platform
Review fundamentals of natural language processing
Show steps
Starting this course with foundational knowledge will enable a stronger understanding of course materials and provide a more solid base for learning more advanced concepts.
Browse courses on
Natural Language Processing (NLP)
Show steps
- Review concepts of syntax and semantics
- Practice extracting named entities using Python libraries like spaCy and NLTK
- Locate a natural language processing library like NLTK and explore its functionality
Implement the Luhn algorithm in Python
Show steps
After learning about the famous Luhn algorithm, implementing it will solidify your understanding and strengthen your Python programming skills.
Show steps
- Locate a Python implementation of the Luhn algorithm
- Replicate the implementation yourself, step by step
- Test your implementation on various text samples
Build a text summarization tool using Python
Show steps
Develop your skills across the board by using the concepts learned in this course to create a practical and tangible tool.
Browse courses on
Text Summarization
Show steps
- Design the architecture of your tool, considering the different summarization algorithms covered in the course
- Implement the tool using Python, integrating the chosen algorithms
- Test and refine your tool on a variety of text samples to ensure its accuracy and efficiency
Contribute to open-source NLP projects
Show steps
Engage with the broader NLP community by contributing to open-source projects, expanding your knowledge and making a tangible impact.
Browse courses on
Open Source
Show steps
- Identify open-source NLP projects that align with your interests
- Explore the project's codebase and documentation
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements
Participate in an NLP hackathon or competition
Show steps
Challenge yourself in a practical and competitive setting, applying your NLP skills to solve real-world problems.
Show steps
- Find an NLP hackathon or competition that aligns with your interests
- Form a team or work individually on developing a solution
- Submit your solution and compete for recognition and prizes
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