| 基于综合的句子特征的文本自动摘要 |
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| 引用本文： | 程 园,吾守尔·斯拉木,买买提依明·哈斯木.基于综合的句子特征的文本自动摘要[J].计算机科学,2015,42(4):226-229. |
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| 作者姓名： | 程 园 吾守尔·斯拉木 买买提依明·哈斯木 |
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| 作者单位： | 1. 新疆大学信息科学与工程学院新疆多语种信息技术实验室 乌鲁木齐830046
2. 新疆大学信息科学与工程学院新疆多语种信息技术实验室 乌鲁木齐830046;和田师范专科学校计算机科学系 和田848000 |
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| 基金项目： | 本文受国家973重点基础研究发展计划资金项目(2014CB340506)资助 |
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| 摘 要： | 采用了一种综合的文本自动摘要方法来抽取出涵盖范围广、冗余信息少、最能反映文本中心思想的文本摘要.该方法充分考虑文本中的词频、标题、句子位置、线索词、提示性短语、句子相似度等特征因素,构建了一个综合的特征加权函数,运用数学回归模型对语料进行训练,去除冗余句子信息,提取关键句生成摘要.实验评估表明了该方法的可行性、有效性以及在摘要质量方面的优越性.
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| 关 键 词： | 自动摘要 特征因素 综合 加权函数 |
Automatic Text Summarization Based on Comprehensive Characteristics of Sentence |
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| CHENG Yuan,Wushouer SILAMU and Maimaitiyiming HASIMUA.Automatic Text Summarization Based on Comprehensive Characteristics of Sentence[J].Computer Science,2015,42(4):226-229. |
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| Authors: | CHENG Yuan Wushouer SILAMU and Maimaitiyiming HASIMUA |
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| Affiliation: | Xinjiang Laboratory of Multi-language Information Technology,School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China,Xinjiang Laboratory of Multi-language Information Technology,School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China and Xinjiang Laboratory of Multi-language Information Technology,School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China;Department of Information Science,Hetian Teather College,Hetian 848000,China |
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| Abstract: | To extract the abstract with less redundant information and a wide coverage,which can reflect the main idea of the text,this paper advanced a comprehensive text summarization method.This method takes the frequency of the words,the title,the position of the sentence in the text,cue phrases,similarity of the sentences and other features in the text into consideration,constructs a comprehensive feature weighting function,trains the corpus with mathematical regression model,removes the redundant information,and then gets the abstract.The experiment shows that this method is very effective and feasible,and very superior in the quality of the extraction. |
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| Keywords: | Automatic abstract Features Comprehensive Weighting function |
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