Automatic text summarization using fuzzy inference

M. Jafari, Jing Wang, Yongrui Qin, M. Gheisari, Amir Shahab Shahabi, Xiaohui Tao
{"title":"Automatic text summarization using fuzzy inference","authors":"M. Jafari, Jing Wang, Yongrui Qin, M. Gheisari, Amir Shahab Shahabi, Xiaohui Tao","doi":"10.1109/IConAC.2016.7604928","DOIUrl":null,"url":null,"abstract":"Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.","PeriodicalId":375052,"journal":{"name":"2016 22nd International Conference on Automation and Computing (ICAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 22nd International Conference on Automation and Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IConAC.2016.7604928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31

Abstract

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.
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
使用模糊推理的自动文本摘要
由于网络上大量的信息和电子文档,人类几乎不可能学习、研究和分析这些大量的文本。总结上下文的主要思想和主要概念,使人类能够快速阅读大量文本的摘要,并决定是否进一步深入研究细节。现有的摘要方法大多采用基于概率和统计的技术。但这些方法不能达到较高的精度。我们观察到,关注上下文的概念和含义可以大大提高摘要的准确性,并且由于摘要方法中存在不确定性,我们在本研究中通过将模糊逻辑与传统统计方法相结合来模拟类人方法。研究结果表明,与现有方法相比,我们的方法可以处理不确定性,并取得更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Comparative study of Partial Discharge emulators for the calibration of Free-Space radiometric measurements Knowledge representation of large medical data using XML An investigation of electrical motor parameters in a sensorless variable speed drive for machine fault diagnosis A novel fault-tolerant control strategy for Near Space Hypersonic Vehicles via Least Squares Support Vector Machine and Backstepping method Automatic text summarization using fuzzy inference
×
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