以主題事件追蹤為基礎之摘要擷取

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王蓮淨(Lian-Jing Wang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以主題事件追蹤為基礎之摘要擷取

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摘要(中)

近年由於網路發展迅速，使用者只要透過網路即可以取得所需資訊，但過多的資訊造成資訊過載之問題，因此如何在眾多資訊中擷取出重要的資訊供使用者閱讀已成為當今重要議題。然而傳統的摘要模式通常為靜態摘要，並無法針對特定主題做每日摘要的動態更新，因此本研究加入了遺忘因子，每日可更新摘要內容。並採用以主題關鍵字為基礎的方式，產出特定主題摘要內容，本研究將使用查詢式摘要(Query-oriented Summarization)法來進行多文件摘要之擷取。
本研究將採用圖形網路分群架構分析文句之間潛在語意關聯性，分群方式為K-Medoids分群，探討圖形網路中所有文句節點之間的相似度，並將之做分群，得出文句間潛在語意，以提升摘要品質。
實驗採用DUC 2002資料集，並以ROUGE衡量摘要品質，和自行蒐集之CNN新聞文章，其主題分別為尼泊爾大地震、伊斯蘭國及MERS，並觀察摘要結果是否能達到主題事件追蹤的功效；經實驗證明，本研究採用K-Medoids分群架構之多文件摘要方法在DUC 2002之50字、100字和200字多文件摘要，ROUGE-1值分別可達到0.2948、0.3435與0.4375，此結果在50字與100字摘要品質幾乎優於全數當年研討會之參賽者之摘要品質，另外200字摘要結果也與當年參賽者勢均力敵；而在主題事件追蹤之摘要實驗，也證實本系統可以達到主題事件追蹤摘要的功效。
關鍵字：查詢式摘要、擷取式摘要、K-Medoids、遺忘因子、多文件摘要、主題事件追蹤。

摘要(英)

In recent year, the developing technology of Network is getting soon. User can get information through the Internet, but it generates a problem that is information overload. Therefore, how to get some important information to user is really important now. However, the traditional technology of summarization is static, and it can′t trace the specific topic and update the summary everyday. That is why there is a damping factor in this research, and it can update the summary everyday. Also, in this research, using a way which based on topic term, and created the summary of the specific topic. In this research, using the Query-oriented Summarization way is to get Multi-document Summarization.
Using the clustering architecture of graph network is to analyze the hiding semantic relation between sentences in this research. The clustering way is K-Medoids Clustering. Discussing the similarly between all sentences in graph network, and clustering these sentences are to get hiding semantic relation between sentences to rise the quality of summary.
In experiment, using DUC 2002 data set and analyzing quality of summary by ROUGE, and the other data set is CNN news which topics are Nepal earthquake, Islamic State, and MERS. Observing the result of summaries is achieving the efficacy which is tracing topic event or not. The result show that using K-Medoids clustering architecture is to create Multi-document Summarizations which are 50, 100 and 200 words by DUC 2002 data set. The results of ROUGE-1 are 0.2948, 0.3435 and 0.4375. Also, the quality of summaries which are 50 and 100 words are higher than participants in DUC 2002. In addition, the result of summary of 200 words is good as participants in DUC 2002. Furthermore, in experiment of summary of tracing topic event, also proving the system in this research can achieve the efficacy which is tracing topic event.
Keywords: Query-oriented Summarization, Extractive Summarization, K-Medoids, damping factor, Multi-document Summarization and tracing topic event

關鍵字(中)

★ 查詢式摘要
★ 擷取式摘要
★ K-Medoids
★ 遺忘因子
★ 多文件摘要
★ 主題事件追蹤

關鍵字(英)

★ Query-oriented Summarization
★ Extractive Summarization
★ K-Medoids
★ damping factor
★ Multi-document Summarization
★ tracing topic event

論文目次

摘要 i
Abstract v
目錄 vii
圖目錄 x
表目錄 xi
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 10
1-4 論文架構 10
二、文獻探討 11
2-1 自動文件摘要 11
2-2 相關文獻做法與本研究差異 12
2-3 NGD 15
2-4 特徵分析方法 16
2-4-1 1-gram filtering 16
2-4-2 文件內容與標題之間關聯性 18
2-4-3 文件內容與主題關鍵字之間關聯性 18
2-4-4 Term Frequency-Inverse Sentence Frequency 18
2-4-5 文句長度之研究 19
2-5 向量相似度衡量方法 19
2-6 K-Medoids文句分群 20
2-7 鏈結評分方法 20
2-7-1 Degree 21
2-7-2 Strength 21
2-7-3 K-Core 21
2-7-4 PageRank 22
2-7-5 Locality Index 22
2-8 波達計數法 23
三、系統架構 24
3-1 系統概念與流程 24
3-2 前處理流程 25
3-2-1 1-gram filtering 25
3-2-2 篩選候選關鍵字 26
3-2-2-1 候選關鍵字 26
3-2-2-2 候選關鍵字向量 27
3-2-2-3 候選關鍵字活躍權重(Active Weight, AW) 27
3-2-2-4 計算候選關鍵字權重 28
3-2-3 文句過濾 29
3-2-4 更新候選關鍵字AW權重 30
3-2-5 文句轉向量 32
3-3 文句計分 33
3-3-1 K-Medoids 文句分群與文句計分 33
3-3-2 建立文句關係網路 33
3-3-3 K-Medoids文句分群實作與群集計分 34
3-3-4 鏈結評分法 36
3-3-4-1 單一鏈結評分法 36
3-3-4-2 整合鏈結評分法 37
3-4 挑選文句 37
四、實驗設計與結果討論 37
4-1 資料集與實驗設置 37
4-1-1 實驗環境 38
4-2 評估摘要成果準則 38
4-3 實驗流程 39
4-4 實驗數據與討論 40
4-4-1 實驗一：使用DUC 2002資料集進行單一鏈結法評估 41
4-4-2 實驗二：使用DUC 2002資料集進行整合鏈結法評估 45
4-4-3 實驗三：使用CNN新聞資料集進行主題事件追蹤之評估 47
4-4-4 實驗四：比較系統效能 58
五、結論以及未來研究方向 60
5-1 結論 61
5-2 未來研究方向 62
參考文獻 63

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指導教授

林熙禎(Shi-Jen Lin)

審核日期

2015-7-27

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