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2022 | OriginalPaper | Chapter

33. Abstractive Text Summarization Using Machine Learning

Authors : Aditya Dingare, Doina Bein, Wolfgang Bein, Abhishek Verma

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter delves into the various types of text summarization, including single document, multi-document, informative summary, and query-focused summary. It differentiates between abstractive and extractive text summarization methods, highlighting the challenges and advantages of each. The authors apply three text summarization algorithms—extractive text summarization using NLTK and TextRank, and abstractive text summarization using Seq-to-Seq—to the Amazon Product Review dataset. The chapter presents a detailed analysis of the algorithms' performances, limitations, and potential future improvements, offering valuable insights for professionals in the field of natural language processing and machine learning.
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Metadata
Title
Abstractive Text Summarization Using Machine Learning
Authors
Aditya Dingare
Doina Bein
Wolfgang Bein
Abhishek Verma
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_33

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