Single-Document Abstractive Text Summarization: A Systematic Literature Review

  • Abishek Rao
  • , Shivani Aithal
  • , Sanjay Singh*
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

9 Citations (Scopus)

Abstract

Abstractive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This article identifies and discusses potential opportunities and directions along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.

Original languageEnglish
Article number60
JournalACM Computing Surveys
Volume57
Issue number3
DOIs
Publication statusPublished - 11-11-2024

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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