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Getting Started with Google BERT

You're reading from   Getting Started with Google BERT Build and train state-of-the-art natural language processing models using BERT

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781838821593
Length 352 pages
Edition 1st Edition
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (15) Chapters Close

Preface 1. Section 1 - Starting Off with BERT
2. A Primer on Transformers FREE CHAPTER 3. Understanding the BERT Model 4. Getting Hands-On with BERT 5. Section 2 - Exploring BERT Variants
6. BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT 7. BERT Variants II - Based on Knowledge Distillation 8. Section 3 - Applications of BERT
9. Exploring BERTSUM for Text Summarization 10. Applying BERT to Other Languages 11. Exploring Sentence and Domain-Specific BERT 12. Working with VideoBERT, BART, and More 13. Assessments 14. Other Books You May Enjoy
Exploring BERTSUM for Text Summarization

Text summarization is one of the most popular applications of natural language processing. In this chapter, we will understand how to fine-tune the pre-trained BERT model for a text summarization task. The BERT model fine-tuned for the text summarization task is often called BERTSUM (BERT for summarization). In this chapter, we will understand what BERTSUM is and how it is used for text summarization in detail.

We will begin the chapter by understanding different types of text summarization called extractive and abstractive summarizations. First, we will learn how to perform extractive summarization using BERTSUM with a classifier, BERTSUM with a transformer, and BERTSUM with an LSTM. Next, we will look into how BERTSUM is used for performing the abstractive summarization task.

Going forward, we will learn about the text summarization...

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