单文档摘要文本总结:系统性文献综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-10-18 DOI:10.1145/3700639
Abishek Rao, Shivani Aithal, Sanjay Singh
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引用次数: 0

摘要

文本摘要是自然语言处理中的一项任务,它能以人工编写的形式自动生成源文件的摘要,并将信息损失降到最低。由于抽象文本摘要的挑战性,文本摘要的研究已经转向抽象文本摘要。本研究对抽象文本摘要的单篇文档摘要进行了广泛系统的文献综述,以深入了解其面临的挑战、广泛使用的数据集、评估指标、方法和手段。本研究综述了 2011 年至 2023 年间在流行电子数据库中发表的研究文章。共有 226 篇期刊和会议出版物被纳入本综述。对这些论文的深入分析有助于研究人员了解所面临的挑战、广泛使用的数据集、评估指标、方法和途径。本文确定并讨论了抽象文本摘要研究的潜在机遇和方向,以及关于抽象摘要模型和技术的通用概念框架和指南。
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Single-Document Abstractive Text Summarization: A Systematic Literature Review
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.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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