- 6 NLP Techniques Every Data Scientist Should Know - Feb 12, 2021.
Natural language processing has already begun to transform to way humans interact with computers, and its advances are moving rapidly. The field is built on core methods that must first be understood, with which you can then launch your data science projects to a new level of sophistication and value.
- Multi-domain summarization by PlexPage - Nov 10, 2020.
The PlexPage by Algoritmi Vision is an Abstractive Multi-domain Search Summarization application built using the unique and innovative structure of the Natural Language Generation (NLG) technique. Learn more here, and try it out for yourself.
- 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out - Sep 9, 2020.
If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.
- Automatic Text Summarization in a Nutshell - Dec 18, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.
- KDnuggets™ News 19:n46, Dec 4: The Future of Data Science Careers; Which Data Visualization Should I Use? - Dec 4, 2019.
This week: The Future of Careers in Data Science & Analysis; Task-based effectiveness of basic visualizations; Open Source Projects by Google, Uber and Facebook for Data Science and AI; Getting Started with Automated Text Summarization; A Non-Technical Reading List for Data Science; and much more!
- Getting Started with Automated Text Summarization - Nov 28, 2019.
This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.
- Approaches to Text Summarization: An Overview - Jan 3, 2019.
This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.