The Repository of Web Document Summarization using Social Information

Minh-Tien Nguyen, Van-Hau Nguyen, Duc-Vu Tran
{"title":"The Repository of Web Document Summarization using Social Information","authors":"Minh-Tien Nguyen, Van-Hau Nguyen, Duc-Vu Tran","doi":"10.1109/KSE.2019.8919378","DOIUrl":null,"url":null,"abstract":"Summarization using social information is a task which extracts summary sentences and relevant user posts of a Web document by integrating its relevant social information. Prior studies introduced several strong models for this task; however, there are gaps from papers to the reproduction of such models. This paper leverages the gaps by investigating summa-rization algorithms to facilitate next studies. The investigation was conducted by implementing traditional and state-of-the-art methods, from unsupervised to supervised learning fashion. We used three datasets in English and Vietnamese to confirm the efficiency of the methods. Experimental results indicate that sophisticated models obtain improvements of ROUGE-scores compared to the basic ones, which do not use social information. However, in some cases, simple methods comparably perform state-of-the-art methods, suggesting that the performance of summarization methods can be still improved.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE.2019.8919378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Summarization using social information is a task which extracts summary sentences and relevant user posts of a Web document by integrating its relevant social information. Prior studies introduced several strong models for this task; however, there are gaps from papers to the reproduction of such models. This paper leverages the gaps by investigating summa-rization algorithms to facilitate next studies. The investigation was conducted by implementing traditional and state-of-the-art methods, from unsupervised to supervised learning fashion. We used three datasets in English and Vietnamese to confirm the efficiency of the methods. Experimental results indicate that sophisticated models obtain improvements of ROUGE-scores compared to the basic ones, which do not use social information. However, in some cases, simple methods comparably perform state-of-the-art methods, suggesting that the performance of summarization methods can be still improved.
基于社会信息的Web文档摘要资源库
社会信息摘要是通过整合Web文档的相关社会信息,提取其摘要语句和相关用户帖子的任务。先前的研究为这项任务引入了几个强有力的模型;然而,从论文到这些模型的复制存在差距。本文通过研究汇总算法来利用这些差距,以促进下一步的研究。调查采用了传统和最先进的方法,从无监督学习到监督学习。我们使用英语和越南语的三个数据集来验证方法的有效性。实验结果表明,与不使用社会信息的基本模型相比,复杂模型的rouge分数得到了提高。然而,在某些情况下,简单方法的性能可以与最先进的方法相媲美,这表明总结方法的性能仍有待提高。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信