Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

Text Summarization

Automated text summarization is the process of using NLP tools to produce concise versions of text that preserve the key information present in the original content. Good summaries can communicate the content with less text by retaining the key information while filtering out other information and noise (or useless text, if any). A shorter text may often take less time to read, and thus summarization facilitates more efficient use of time.

The type of summarization that we are typically taught in school is abstractive summarization. One way to think of this is to consider abstractive summarization as a combination of understanding the meaning and expressing it in fewer sentences. It is usually considered as a supervised learning problem as the original text and the summary are both required. However, a piece of text can be summarized in more than one way. This makes it hard to teach the machine in a general way. While abstractive summarization is an active area...