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Advanced Natural Language Processing with TensorFlow 2

You're reading from   Advanced Natural Language Processing with TensorFlow 2 Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800200937
Length 380 pages
Edition 1st Edition
Languages
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Authors (2):
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Tony Mullen Tony Mullen
Author Profile Icon Tony Mullen
Tony Mullen
Ashish Bansal Ashish Bansal
Author Profile Icon Ashish Bansal
Ashish Bansal
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Toc

Table of Contents (13) Chapters Close

Preface 1. Essentials of NLP 2. Understanding Sentiment in Natural Language with BiLSTMs FREE CHAPTER 3. Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding 4. Transfer Learning with BERT 5. Generating Text with RNNs and GPT-2 6. Text Summarization with Seq2seq Attention and Transformer Networks 7. Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks 8. Weakly Supervised Learning for Classification with Snorkel 9. Building Conversational AI Applications with Deep Learning 10. Installation and Setup Instructions for Code 11. Other Books You May Enjoy
12. Index

Overview of text summarization

The core idea in summarization is to condense long-form text or articles into a short representation. The shorter representation should contain the main idea of crucial information from the longer form. A single document can be summarized. This document could be long or may contain just a couple of sentences. An example of a short document summarization is generating a headline from the first few sentences of an article. This is called sentence compression. When multiple documents are being summarized, they are usually related. They could be the financial reports of a company or news reports about an event. The generated summary could itself be long or short. A shorter summary would be desirable when generating a headline. A lengthier summary would be something like an abstract and could have multiple sentences.

There are two main approaches when summarizing text:

  • Extractive summarization: Phrases or sentences from the articles are selected...
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