Natural Language Processing for Text Summarization

Natural Language Processing for Text Summarization, available at $69.99, has an average rating of 4.34, with 44 lectures, based on 365 reviews, and has 15818 subscribers.

You will learn about 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 This course is ideal for individuals who are People interested in natural language processing and text summarization or People interested in the spaCy and NLTK libraries or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in natural language processing or Professionals interested in developing text summarization solutions or Beginners who are starting to learn natural language processing It is particularly useful for People interested in natural language processing and text summarization or People interested in the spaCy and NLTK libraries or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in natural language processing or Professionals interested in developing text summarization solutions or Beginners who are starting to learn natural language processing.

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Summary

Title: Natural Language Processing for Text Summarization

Price: $69.99

Average Rating: 4.34

Number of Lectures: 44

Number of Published Lectures: 44

Number of Curriculum Items: 44

Number of Published Curriculum Objects: 44

Original Price: $19.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Introduction to natural language processing

Lecture 3: Source code and slides

Chapter 2: Frequency-based algorithm

Lecture 1: Plan of attack

Lecture 2: Algorithm – intuition

Lecture 3: Preprocessing the texts 1

Lecture 4: Preprocessing the texts 2

Lecture 5: Word frequency

Lecture 6: Weighted word frequency

Lecture 7: Sentence tokenization

Lecture 8: Generating the summary

Lecture 9: Visualizing the summary in HTML

Lecture 10: Extracting texts from the Internet

Lecture 11: Function to summarize the texts

Lecture 12: Function to visualize the results

Lecture 13: Summarizing multiple texts

Chapter 3: Luhn algorithm

Lecture 1: Plan of attack

Lecture 2: Preparing the environment

Lecture 3: Implementation 1

Lecture 4: Implementation 2

Lecture 5: Implementation 3

Lecture 6: Implementation 4

Lecture 7: Implementation 5

Lecture 8: Extracting texts from the Internet

Lecture 9: Reading articles from RSS feeds

Lecture 10: Word cloud

Lecture 11: Extracting named entities

Lecture 12: Summarizing articles from feed

Lecture 13: Summary in HTML files

Chapter 4: Cosine similarity

Lecture 1: Plan of attack

Lecture 2: Preparing the environment

Lecture 3: Similarity between sentences 1

Lecture 4: Similarity between sentences 2

Lecture 5: Similarity matrix

Lecture 6: Summarizing texts

Lecture 7: Extracting texts from the Internet

Chapter 5: Libraries for text summarization

Lecture 1: Plan of attack

Lecture 2: Preparing the environment

Lecture 3: Sumy library

Lecture 4: Pysummarization library

Lecture 5: BERT summarizer library

Lecture 6: Additional content: abstractive summarization

Chapter 6: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 7 votes

2 stars: 6 votes

3 stars: 38 votes

4 stars: 131 votes

5 stars: 183 votes

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