利用文句關係網路自動萃取文件摘要之研究; Using Sentence Network to Automatic Document Summarization

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/61091

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/61091

Title:	利用文句關係網路自動萃取文件摘要之研究;Using Sentence Network to Automatic Document Summarization
Authors:	楊佩臻;Yang,Pei-Chen
Contributors:	資訊管理學系
Keywords:	自動文件摘要;文句關係網路;圖形化摘要方法;NGD;Graph-based Summarization
Date:	2013-07-16
Issue Date:	2013-08-22 12:11:40 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究提出一個使用基於NGD的通用萃取式圖形化摘要方法，由於NGD擁有只需要文件本身資訊及搜尋引擎搜尋結果數的特點，可除去對外部資源如語料庫及語義辭典的依賴。本研究使用NGD計算文件內字詞之間的關聯找出文件關鍵字並用其建立一向量空間，以文句在向量空間中的餘弦相似度為基準建立文句關係網路，再利用鏈結分析找出文句關係網路中重要的文句節點作為摘要。經ROUGE評估摘要品質，本研究所提出利用文句關係網路計分的方法在單文件摘要及50字的多文件摘要中，可達到比DUC2001及DUC2002當年利用機器學習摘要組合的方法更佳的結果，而在100字及200字的多文件摘要中，也僅略遜於當年利用機器學習的幾位參賽者。證明本研究確實建立一有效的不需要依賴相關語料庫及語義辭典的通用非監督式文件萃取式摘要方法。 This paper proposed a Graph-based Summarization method by building a sentence network that represent the relation between sentences with NGD. The method can get rid of the dependence of external resources like corpus and lexical database by using the words in the documents and the search result. Using Wiki Engine to calculate NGD and find out the relation between words. Finally, the keywords in the documents are found out. Building a Vector Space Model by the keywords and calculating the similarity between sentences to build a sentence network. The most import sentences are extracted by using Link Analysis. The experiment results showed that the ROUGE value of proposed graph-based single-document summarization method is better than other machine learning methods, and the ROUGE value of proposed graph-based multi-documents summarization method is just lower than few peers using machine learning methods. It proves that this proposed method is an effective unsupervised document summarization without external resources like corpus and lexical database.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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