Natural Language Processing for Text Summarization

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.