What you would learn in Natural Language Processing for Text Summarization course?
The field of natural Language Processing (NLP) is a subset of artificial intelligence, which aims to develop computers capable of comprehending human language, spoken and written. Examples of practical applications include translators between languages, translation of speech in the text to chatbots, automated systems for answering questions (Q&A) and automatic description generation for pictures, creation of subtitles for videos, and classification of emotions in sentences; and many more! Another crucial application is automatic summarization of documents, which is the process of creating texts summaries. If you have to read an article with 50 pages, but you don't have the time to read the entire text. In this situation, you could use an algorithm for summary to create an article's summary. The size of the resume can be altered: you can transform 50 pages into 20 pages, which contain the essential portions in the content!
On this basis, the course covers the theory and the implementation in practice of three algorithms for text summarization: (i) frequency-based, (ii) distance-based (cosine similarity to Pagerank) in addition to (iii) the well-known and well-known Luhn algorithm that was one of the first experiments in this field. In the course, we will implement each algorithm in a step-by-step manner using the latest technology, like Python, the Python programming language as well as using the NLTK (Natural Language Toolkit) and spaCy libraries, as well as Google Colab, which will guarantee that you do not have any issues installing or settings to software installed on your personal computer.
When you finish the course, you'll be able to build an algorithm for summarizing your data! Alongside implementing the algorithms, you'll also be taught how to take news articles from blogs and feeds and generate intriguing summaries using HTML! After implementing the algorithms starting from scratch, you can create another module to utilize specific libraries for composing documents, like Sumy, by summarization or BERT summary. If you've never heard of summarizing text, this course is perfect for those new to the field! On the other hand, is familiar with the subject using text summarization, you can take this course to refresh the fundamentals.
Content of the Course:
Learn about the mathematical theory and calculations that are involved in text summarization algorithms.
Implement the summarized algorithms listed below step by process in Python. The algorithms are frequency-based, distance-based, the classic Luhn algorithm.
Utilize the following libraries for the summarization of texts: Sumy BERT summarizer and by outline.
Condense articles gleaned from feeds and websites.
Utilize the NLTK and spaCy libraries as well as Google Colab to build the natural language processing applications
Create HTML visualizations to present of summary
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