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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
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Sohom Ghosh
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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

High-Level View of Text Summarization

We can look at the topic of text summarization from different angles. The following figure shows how we will proceed:

Figure 6.1: This diagram shows the ways in which we can classify text summarization

We will discuss each aspect of text suammarization in detail in the coming sections.

Purpose

There are a number of different angles from which we can view the topic of text summarization. First, we can look at the purpose of text summarization, where we are interested in knowing what people generally use it for. This is further divided into three parts:

  • Generic summarization: Text summarizers can operate on any text input of sufficient length and they can perform adequately on sources from many different domains. If your project objectives do not care what the domain is and can accept sources from many different fields, then you can use generic summarizers, possibly provided in a tool such as Gensim.
  • Domain...
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