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A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning.

Mengli Zhang1, Gang Zhou1, Wanting Yu1

  • 1State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China.

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Deep learning models excel at automatic text summarization (ATS), generating concise summaries from large datasets. This survey offers a comprehensive overview of deep learning-based abstractive summarization techniques and future research directions.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • The exponential growth of web data presents challenges for information management.
  • Automatic Text Summarization (ATS) is crucial for distilling key information.
  • Deep learning (DL) models are increasingly dominant in ATS.

Purpose of the Study:

  • To provide a comprehensive survey of deep learning-based abstractive text summarization.
  • To bridge the gap in existing literature reviews.
  • To offer insights for researchers in the field.

Main Methods:

  • Overview of abstractive summarization and deep learning concepts.
  • Summarization of typical deep learning frameworks for abstractive summarization.
  • Comparison of popular datasets for training and evaluation.

Main Results:

  • Analysis of the performance of various abstractive summarization systems.
  • Identification of common datasets and their characteristics.
  • Highlighting of state-of-the-art deep learning approaches.

Conclusions:

  • Deep learning is central to modern abstractive summarization.
  • Further research is needed to address open challenges.
  • Future trends point towards advancements in DL-based summarization techniques.