Multi-document Relationship Model for a same subject and its application in automatic summarization

Hao Bai, De-xiang Zhou
{"title":"Multi-document Relationship Model for a same subject and its application in automatic summarization","authors":"Hao Bai, De-xiang Zhou","doi":"10.1109/CINC.2010.5643796","DOIUrl":null,"url":null,"abstract":"In this paper, we proposed Multi-document Relationship Model for a same subject and applicated it in automatic summarization. By using the relationship between text units in different level and information of time and sequence of event contained into document set, this model fuse many documents to extract summarization automatically under not reducing the information in original documents. This model simplied the triditional model presented by cross structure theory and simultaneously, replenish the evolution and distribution information of subject which lacked in information fusion. This paper gives some algorithm about construction of the model, information fusion for multi-document and summarization extraction and so on. Experiment results implied that the model proposed in this paper can solve the problem of summarization extraction for multi-document very well.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643796","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

In this paper, we proposed Multi-document Relationship Model for a same subject and applicated it in automatic summarization. By using the relationship between text units in different level and information of time and sequence of event contained into document set, this model fuse many documents to extract summarization automatically under not reducing the information in original documents. This model simplied the triditional model presented by cross structure theory and simultaneously, replenish the evolution and distribution information of subject which lacked in information fusion. This paper gives some algorithm about construction of the model, information fusion for multi-document and summarization extraction and so on. Experiment results implied that the model proposed in this paper can solve the problem of summarization extraction for multi-document very well.
同一主题的多文档关系模型及其在自动摘要中的应用
本文提出了同一主题的多文档关系模型,并将其应用于自动摘要中。该模型利用不同层次的文本单元之间的关系以及文档集中所包含的时间和事件顺序信息,在不减少原始文档信息的前提下,融合多篇文档自动提取摘要。该模型简化了交叉结构理论提出的传统模型,同时补充了信息融合中缺乏的主体演化和分布信息。本文给出了模型构建、多文档信息融合、摘要提取等算法。实验结果表明,本文提出的模型可以很好地解决多文档摘要抽取问题。
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:481959085
Book学术官方微信