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Reinforcement Learning with TensorFlow

You're reading from  Reinforcement Learning with TensorFlow

Product type Book
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Sayon Dutta Sayon Dutta
Profile icon Sayon Dutta
Toc

Table of Contents (21) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Deep Learning – Architectures and Frameworks 2. Training Reinforcement Learning Agents Using OpenAI Gym 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 1. Further topics in Reinforcement Learning 2. Other Books You May Enjoy Index

Text summarization


Text summarization is the process of automatically generating summarized text of the document test fed as an input by retaining the important information of the document. Text summarization condenses a big set of information in a concise manner; therefore, summaries play an important role in applications related to news/articles, text search, and report generation.

There are two types of summarization algorithms:

  • Extractive summarization: Creates summaries by copying parts of the text from the input text
  • Abstractive summarization: Generates new text by rephrasing the text or using new words that were not in the input text

The attention-based encoder decoder model created for machine translation (Bahdanau et al., 2014)is a sequence-to-sequence model and was able to generate abstractive summaries with good performance by achieving good ROUGE score (seeAppendix A, Further topics in Reinforcement Learning). The performance was good on short input sequences and it deteriorated...

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