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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

Building the revised approach


Now we will be coding the algorithm that we discussed in the previous section. After implementing it, we will check how well or badly our algorithm is performing. This algorithm is easy to implement, so let's begin with the code. You can find the code at this GitHub link: https://github.com/jalajthanaki/medical_notes_extractive_summarization/tree/master/Revised_approach.

Implementing the revised approach

In this section, we will be implementing the summarization algorithm step by step. These are the functions that we will be building here:

  • The get_summarized function

  • The reorder_sentences function

  • The summarize function

Let's begin with the first one.

The get_summarized function

Basically, this function performs the summarization task. First, it will take the content of the document as input in the form of string. After that, this function generates the frequency of the words, so we need to tokenize the sentences into words. After that, we will be generating the top...

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