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Abstractive Text Summarization: State of the Art, Challenges, and Improvements

Hassan Shakil, Ahmad Farooq, Jugal Kalita

TL;DR

This survey analyzes the landscape of abstractive text summarization, outlining five main technique families: traditional Seq2Seq, pre-trained language models, reinforcement learning, hierarchical methods, and multimodal summarization. It highlights key challenges such as inadequate meaning representation, factual consistency, long document handling, and evaluation metrics, and discusses proposed solutions including knowledge integration and advanced training objectives. The authors provide a comparative view of state-of-the-art methods in terms of model complexity, scalability, and application domains, and chart future research directions like cross-lingual, domain-specific, and multimodal summarization. The work aims to guide researchers and practitioners toward more accurate, coherent, and trustworthy abstractive summarization systems.

Abstract

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.

Abstractive Text Summarization: State of the Art, Challenges, and Improvements

TL;DR

This survey analyzes the landscape of abstractive text summarization, outlining five main technique families: traditional Seq2Seq, pre-trained language models, reinforcement learning, hierarchical methods, and multimodal summarization. It highlights key challenges such as inadequate meaning representation, factual consistency, long document handling, and evaluation metrics, and discusses proposed solutions including knowledge integration and advanced training objectives. The authors provide a comparative view of state-of-the-art methods in terms of model complexity, scalability, and application domains, and chart future research directions like cross-lingual, domain-specific, and multimodal summarization. The work aims to guide researchers and practitioners toward more accurate, coherent, and trustworthy abstractive summarization systems.

Abstract

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.
Paper Structure (56 sections, 7 figures, 9 tables)

This paper contains 56 sections, 7 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Prior Surveys on Abstractive Summarization
  3. Organization
  4. Automatic Summmarization
  5. Extractive Summarization
  6. Abstractive Summarization
  7. State of the Art in Abstractive Text Summarization
  8. Traditional Sequence-to-Sequence (Seq2Seq) Models
  9. Basic Seq2Seq models
  10. Attention Mechanisms
  11. Copy Mechanism
  12. Pointer Networks
  13. Pre-trained Large Language Models
  14. BERT
  15. GPT
  16. ...and 41 more sections

Figures (7)

  • Figure 1: Taxonomy of State-of-the-art Abstractive Text Summarization
  • Figure 2: Traditional Seq2Seq model flow for abstractive text summarization
  • Figure 3: Pre-trained Large Language Model flow for abstractive summarization
  • Figure 4: Reinforcement Learning approaches flow for abstractive summarization
  • Figure 5: Hierarchical approaches flow for abstractive summarization
  • ...and 2 more figures