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From Extractive to Abstractive Summarization: A Journey

  • Parth Mehta
  • Prasenjit Majumder
Book

Table of contents

  1. Front Matter
    Pages i-xi
  2. Parth Mehta, Prasenjit Majumder
    Pages 1-9
  3. Parth Mehta, Prasenjit Majumder
    Pages 11-24
  4. Parth Mehta, Prasenjit Majumder
    Pages 25-34
  5. Parth Mehta, Prasenjit Majumder
    Pages 35-48
  6. Parth Mehta, Prasenjit Majumder
    Pages 49-68
  7. Parth Mehta, Prasenjit Majumder
    Pages 83-95
  8. Parth Mehta, Prasenjit Majumder
    Pages 97-98
  9. Back Matter
    Pages 99-116

About this book

Introduction

This book describes recent advances in text summarization, identifies remaining gaps and challenges, and proposes ways to overcome them. It begins with one of the most frequently discussed topics in text summarization –  ‘sentence extraction’ –, examines the effectiveness of current techniques in domain-specific text summarization, and proposes several improvements. 
In turn, the book describes the application of summarization in the legal and scientific domains, describing two new corpora that consist of more than 100 thousand court judgments and more than 20 thousand scientific articles, with the corresponding manually written summaries. The availability of these large-scale corpora opens up the possibility of using the now popular data-driven approaches based on deep learning. The book then highlights the effectiveness of neural sentence extraction approaches, which perform just as well as rule-based approaches, but without the need for any manual annotation. As a next step, multiple techniques for creating ensembles of sentence extractors – which deliver better and more robust summaries – are proposed. In closing, the book presents a neural network-based model for sentence compression. Overall the book takes readers on a journey that begins with simple sentence extraction and ends in abstractive summarization, while also covering key topics like ensemble techniques and domain-specific summarization, which have not been explored in detail prior to this.

Keywords

Automatic Text Summarization Legal document summarization Scientific document summarization Ensemble techniques Neural summarization

Authors and affiliations

  1. 1.Information Retrieval and Language Processing LabDhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia
  2. 2.Information Retrieval and Language Processing LabDhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-8934-4
  • Copyright Information Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-13-8933-7
  • Online ISBN 978-981-13-8934-4
  • Buy this book on publisher's site
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