南京大学学报(自然科学版) ›› 2011, Vol. 47 ›› Issue (4): 398–406.

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 基于维基语义的多文档文摘研究*

 龚书**,瞿有利,田盛丰
  

  • 出版日期:2015-04-21 发布日期:2015-04-21
  • 作者简介: (北京交通大学计算机与信息技术学院,北京,100044)
  • 基金资助:
     教育部科学技术研究重点项日(108126),国家自然科学基金(1087109 / a0107)

 Multi-documents summarization utilizing semantics in Wikipedia

 Gong Shu,Qu You- Li Tian Shenh Fenh
  

  • Online:2015-04-21 Published:2015-04-21
  • About author: (School of Computer and InformationTechnology, Beijing Jiaotong University, Beijing, 100044,China)

摘要:

Abstract:  As an importance technique of natural language processing, multi-documents summarization can facilitate users,information retrieval processes.As the
documents in a collection arc always collected from different resources,there exist ahundant and also complex semantic relations inside a document collection. It’s hard for the
widely used word-based text representation to provide sufficient and accurate information for semantic analysis in summarization process.Thus, we try to use Wikipedia, which has extensive concepts coverage
to extract the concept based representation of documents. We assess the importance of concepts using both global and local information.The global relatedness of concepts is based on Wikipedia’s link structure, while the local relatedness is
calculated based on concepts’co-occurrence m sentence.Three wild-based features arc proposed:The first one is the widely used sentence salience feature based on Markov Chain. The other two are hoth hascd on sentence
similarity with first paragraphs of concept articles in Wikipedia, but one using all concepts occurring in collection while the other using only other contained in sentence itself. Finally we linearly combined these features to select
important sentences, which arc then concatenated to form summary. We compared these features in experiments, and proved that the first paragraph of related concepts’Wikipedia articles can bring better summary quality.

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