ive 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 paper 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.

中文翻译：

---

单文献抽象文本摘要：系统文献综述


IVE 文本摘要是自然语言处理中的一项任务，它以人工编写的形式从源文档自动生成摘要，同时将信息损失降至最低。由于其具有挑战性的方面，文本摘要的研究已转向抽象文本摘要。本研究对单文档摘要的抽象文本摘要进行了广泛的系统文献综述，以深入了解挑战、广泛使用的数据集、评估指标、方法和方法。本研究回顾了 2011 年至 2023 年间从流行的电子数据库中发表的研究文章。本综述共纳入 226 篇期刊和会议出版物。对这些论文的深入分析有助于研究人员了解挑战、广泛使用的数据集、评估指标、方法和方法。本文确定并讨论了潜在的机会和方向，以及抽象摘要模型和抽象文本摘要研究技术的通用概念框架和指南。