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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Basic Feature Extraction Methods 3. Developing a Text classifier 4. Collecting Text Data from the Web 5. Topic Modeling 6. Text Summarization and Text Generation 7. Vector Representation 8. Sentiment Analysis Appendix

6. Text Summarization and Text Generation

Activity 11: Summarizing a Downloaded Page Using the Gensim Text Summarizer

Solution

Let's summarize a downloaded page with the help of the Gensim text summarizer. Follow these steps to implement this activity:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to import the necessary libraries:
    import warnings
    warnings.filterwarnings('ignore')
    from gensim.summarization import summarize
    import requests
  3. The following code uses the requests library to get the Why Click page. After getting the page, we change the encoding to utf-8 in order to properly decode some of the content on the page. Then, we use BeautifulSoup to find the text content of the div with the ID #why-click. This div contains the main text of the why-click page:
    from bs4 import BeautifulSoup 
    r = requests.get('https://click.palletsprojects.com/en/7.x/why/')
    r.encoding = 'utf-8'
    soup = BeautifulSoup(r.text...
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