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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Understanding the basics of summarization


In this section, we will be focusing on the basic concepts of summarization. In today's fast-growing information age, text summarization has become an important tool. It will be difficult for humans to generate a summary for large text documents. There are lots of documents available on the web today. So, we need a solution that can automatically generate a summary for documents efficiently, accurately, and intelligently. This task is referred to as automatic text summarization.

Automatic text summarization is all about finding relevant information from the large text document in a small amount of time. Basically, there are two types of summarization:

  • Extractive summarization

  • Abstractive summarization

Let's look at the types of summarization one by one.

Extractive summarization

In the extractive summarization method, we will be generating a summary of the document by selecting words, phrases, or sentences from the original document. We will be using concepts...

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