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
{"title":"单文档摘要文本总结:系统性文献综述","authors":"Abishek Rao, Shivani Aithal, Sanjay Singh","doi":"10.1145/3700639","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":23.8000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single-Document Abstractive Text Summarization: A Systematic Literature Review\",\"authors\":\"Abishek Rao, Shivani Aithal, Sanjay Singh\",\"doi\":\"10.1145/3700639\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":50926,\"journal\":{\"name\":\"ACM Computing Surveys\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":23.8000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Computing Surveys\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3700639\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3700639","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures Security and Privacy on Generative Data in AIGC: A Survey Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models Fog Computing Technology Research: A Retrospective Overview and Bibliometric Analysis Evaluation Methodologies in Software Protection Research
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1