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題名：應用語句關係網路計算語句向心性之新聞事件摘要方法
書刊名：資訊管理學報
作者：葉鎮源／楊維邦 引用關係／柯皓仁 引用關係／鄭培成 引用關係
作者(外文)：Yeh, Jen-yuan／Yang, Wei-pang／Ke, Hao-ren／Cheng, Pei-cheng
出版日期：2014
卷期：21:3
頁次：頁271-304
主題關鍵詞：多文件摘要；摘錄式摘要；語句關係網路；網路節點向心性；語句排序；Multidocument summarization；Extraction-based summarization；Sentence similarity network；Network-based sentence centrality；Sentence ranking
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摘錄式摘要技術的核心在於評估語句的摘要代表性，藉以排序語句作為摘錄 語句時的依據。本研究將語句視為節點，藉由語句相似度來決定節點間是否存在 連結，依此建構出語句關係網路模型。接著，衡量節點在網路中的重要性或對於 其他相連節點的影響性，提出：(1) Degree Centrality、(2) Normalized Similarity-based Degree Centrality、(3) HITS Centrality、(4) PageRank Centrality，及(5) iSpreadRank Centrality 的節點向心性分析；並以語句向心性作為語句的摘要代表性，藉此達到 排序語句的目的。最後，導入CSIS（Cross-Sentence Information Sub-sumption）過 濾重複性資訊，依序擷取語句組成摘要。實驗使用DUC 2004 資料集來驗證上述摘 要方法的可行性。在ROUGE-1 的指標下，結合不同語句向心性之摘要效能依序 是：iSpreadRank > Normalized Similarity-based Degree > PageRank > Degree > HITS。整體而言，實驗得知應用語句關係網路計算語句向心性之摘要方法確實可 行。
Purpose: One widely-adopted summarization paradigm, sentence extraction, aims at extracting important sentences and composing them into a summary. The foundation towards sentence extraction is to assess importance of sentences in the summary so as to rank sentences for extraction. This paper employs graph-based text analysis to model documents and investigates measures of graph-based centrality as sentence salience in summarization. Design/methodology/approach: This paper models documents on the same (or related) topic as a sentence similarity network, in which a sentence is regarded as a node and relationship between sentences only exists if they are semantically related. Severalmethods for evaluating the importance of a node (i.e., a sentence) in the network are then proposed, namely: (1) Degree Centrality; (2) Normalized Similarity-based Degree Centrality; (3) HITS Centrality; (4) PageRank Centrality; and (5) iSpreadRank Centrality. All are designed on the basis of the idea that the importance of a node is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. As to summary generation, CSIS (Cross-Sentence Information Sub-sumption) is employed for anti-redundancy while extracting sentences according to the sentence ranking produced based on the centrality of sentences. Findings: The proposed summarization method was evaluated using the ROUGE evaluation suite on the DUC 2004 news stories collection. Experimental results show that, while considering the ROUGE-1 metric, the performance ranking is: iSpreadRank > Normalized Similarity-base Degree > PageRank > Degree > HITS. Another experiment, conducted to combine sentence centrality with surface-level features, also presents competitive results, compared with the best participant in the DUC 2004 evaluation. Research limitations/implications: Directions for future research would be: (1) instead of symbolic-level analysis, to take into account semantics, such as synonymy, polysemy, and term dependency, while determining if two sentences are semantically related; (2) to investigate graph-based centrality developed in social network analysis for evaluating sentence salience in summarization; (3) to improve the cohesion andcoherence of summaries using natural language processing techniques, such as sentence planning and generation. Practical implications: The proposed summarization method is in an unsupervised manner; thus no training dataset is required. Since no domain-specific knowledge or deep linguistic analysis is exploited, the method is domain- and language-independent. However, it might lead to poor understanding of the input texts and would probably produces poor summaries, due to neither deep analysis of natural language processing performed, discourse structure considered, nor domain-specific knowledge involved in the process of summarization, Originality/value: The contributions of this work are threefold. First, this paper offers a sentence similarity network to model topic-related documents. Second, novelgraph-based sentence ranking methods are explored to rank the importance of sentences for extraction. Finally, the proposed method had been proven successful in a case study with the DUC 2004 benchmark dataset.
期刊論文
1.Kleinberg, Jon M.(1999)。Authoritative sources in a hyperlinked environment。Journal of the ACM，46(5)，604-632。  new window
2.Aliguliyev, R. M.(2010)。Clustering techniques and discrete particle swarm optimization algorithm for multi-document summarization。Computational Intelligence，26(4)，420-448。  new window
3.Boudin, F.、Huet, S.、Torres-Moreno, J.M.(2011)。A graph-based approach to cross-language multi-document summarization。Polibits，43，113-118。  new window
4.Cai, X.-Y.、Li, W.-J.(2013)。Ranking through clustering: an integrated approach to multi-document summarization。IEEE Transactions on Audio, Speech, and Language Processing，21(7)，1424-1433。  new window
5.Erkan, Gunes、Radev, Dragomir R.(2004)。LexRank: Graph-based Lexical Centrality As Salience in Text Summarization。Journal of Artificial Intelligence Research，22(1)，457-479。  new window
6.Mani, I.、Bloedorn, E.(1999)。Summarizing similarities and difference among related documents。Information Retrieval，1(1-2)，35-67。  new window
7.McDonald, D. M.、Chen, H.(2006)。Summary in context: searching versus browsing。ACM Transactions on Information Systems，24(1)，111-141。  new window
8.Li, J.、Li, S.(2013)。A novel feature-based Bayesian model for query focused multi-document summarization。Transactions of the Association for Computational Linguistics，1，89-98。  new window
9.Sathish kumar, T.、Sharmila, V.(2013)。An efficient document summarization using adaptive ranking clustering scheme。International Journal of Computer Technology & Applications，4(6)，1052-1054。  new window
10.Suanmali, L.、Salim, N.、Binwahlan, M.S.(2011)。Genetic algorithm based sentence extraction for text summarization。Journal of Innovative Computing，1(1)，1-22。  new window
11.Anderson, J. R.(1983)。A spreading activation theory of memory。Journal of Verbal Learning and Verbal Behavior，22，261-295。  new window
12.Gupta, V.、Chauhan, P.、Garg, S.(2012)。An statistical tool for multi-document summarization。Journal of Scientific and Research Publications，2(5)，1-5。  new window
13.Luhn, H. P.(1958)。The Automatic Creation of Literature Abstracts。IBM Journal of Research and Development，2(2)，159-165。  new window
14.Brin, Sergey、Page, L.(1998)。The Anatomy of a Large-scale Hypertextual Web Search Engine。Computer Networks and ISDN Systems，30(1-7)，107-117。  new window
15.Porter, M. F.(1980)。An Algorithm for Suffix Stripping。Program: Electronic Library and Information Systems，14(3)，130-137。  new window
16.Collins, A. M.、Loftus, E. F.(1975)。A spreading-activation theory of semantic processing。Psychological Review，82(6)，407-428。  new window
17.Salton, G.、Buckley, C.(1988)。Term-weighting approaches in automatic text retrieval。Information Processing & Management: an International Journal，24(5)，513-523。  new window
18.Salton, G.、Singhal, A.、Mitra, M.、Buckley, C.(1997)。Automatic Text Structuring and Summarization。Information Processing & Management，33(2)，193-207。  new window
19.Yeh, J. Y.、Ke, H. R.、Yang, W. P.、Meng, I. H.(2005)。Text Summarization Using a Trainable Summarizer and Latent Semantic Analysis。Information Processing and Management，41(1)，75-95。  new window
20.Radev, D. R.、Jing, H.、Sty, M.、Tam, D.(2004)。Centroid-based summarization of multiple documents。Information Processing and Management，40，919-938。  new window
會議論文
1.Barzilay, R.、Elhadad, M.(1997)。Using lexical chains for text summarization。ACL/EACL'97 Workshop on Intelligent Scalable Text Summarization，(會議日期: 1997, July 11)。Madrid。10-17。  new window
2.Barzilay, R.、McKeown, K.R.、Elhadad, M.(1999)。Information fusion in the context of multi-document summarization。ACL 1999，(會議日期: June 20-26)。College Park, MD。550-557。  new window
3.Canhasi, E.、Kononenko, I.(2011)。Semantic role frames graph-based multidocument summarization。14th International Multiconference on Information Society，(會議日期: October 10-14)。Ljubljana, Slovenia。113-116。  new window
4.Christensen, J.、Mausam, Soderland, S.、Etzioni, O.(2013)。Towards coherent multi-document summarization(會議日期: June 9-14)。Atlanta, GA。1163-1173。  new window
5.Daniel, N.、Radev, D.、Allison, T.(2003)。Sub-event based multidocument summarization(會議日期: May 30)。Edmonton, Canada。9-16。  new window
6.Erkan, G.(2006)。Using biased random walks for focused summarization。DUC 2006 Document Understanding Workshop，(會議日期: June 8-9)。Brooklyn, NY。  new window
7.Goldstein, J.、Mittal, V.、Carbonell, J.、Kantrowitz, M.(2000)。Multi-document summarization by sentence extraction(會議日期: April 30)。Seattle, WA。40-48。  new window
8.Harabagiu, S.、Maiorano, S.(2002)。Multi-document summarization with GIS Texter。LREC 2002，(會議日期: May 29-31)。Canary Islands, Spain。1456-1463。  new window
9.Haveliwala, T.H.(2002)。Topic-sensitive Page Rank。WWW 2002，(會議日期: May 7-11)。Honolulu, HI。517-526。  new window
10.McKeown, K.R.、Klavans, J.L.、Hatzivassiloglou, V.、Barzilay, R.、Eskin, E.(1999)。Towards multidocument summarization by reformulation: progress and prospects。AAAI 1999，(會議日期: July 18-22)。Orlando, FL。453-460。  new window
11.McKeown, K.、Radev, D.R.(1995)。Generating summaries of multiple news articles。SIGIR 1995，(會議日期: July 09)。Seattle, WA。74-82。  new window
12.Mihalcea, R.(2004)。Graph-based ranking algorithms for sentence extraction, applied to text summarization(會議日期: July 21-26)。Barcelona, Spain。170-173。  new window
13.Mihalcea, R.、Tarau, P.(2005)。An algorithm for language independent single and multiple document summarization(會議日期: October 11-13)。Jeju Island, Korea。19-24。  new window
14.Lin, C.Y.、Hovy, E.(2003)。Automatic evaluation of summaries using N-gram co-occurrence statistics。HLT-NAACL 2003，(會議日期: May 27 - June 1)。Edmonton, Canada。71-78。  new window
15.Wan, X.、Yang, J.(2006)。Improved affinity graph based multi-document summarization。The 2006 Human Language Technology Conference-North American Chapter of the Association for Computational Linguistics Annual Meeting，(會議日期: 2006/06/04-06/09)。New York, NY。181-184。  new window
16.Xia, Y.、Zhang, Y.、Yao, J.(2011)。Co-clustering sentences and terms for multi-document summarization。CICLing 2011，(會議日期: February 20-26)。Tokyo, Japan。339-352。  new window
17.Zhang, J.、Sun, L.、Zhou, Q.(2005)。A cue-based hub-authority approach for multi-document text summarization(會議日期: August 21-23)。Wuhan, China。21-23。  new window
18.Zhu, X.、Goldberg, A.B.、Van Gael, J.、Andrzejewski, D.(2007)。Improving diversity in ranking using absorbing random walks。HLT-NAACL 2007，(會議日期: April 22-27)。Rochester, NY。97-104。  new window
19.陳光華(1998)。新資訊時代的啟發性資訊服務。臺北：桃園。195-208。  延伸查詢new window
20.Wan, X.、Yang, J.(2008)。Multi-document summarization using cluster-based link analysis。Annual International ACM Conference on Research and Development in Information Retrieval，299-306。  new window
21.Gong, Y.、Liu, X.。Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis。SIGIR 2001，(會議日期: September 9-13, 2001)。New York, N. Y.。19-25。  new window
學位論文
1.謝仰哲(2008)。國中生友誼與學習諮詢網路之社會網路分析(碩士論文)。國立臺灣師範大學。  延伸查詢new window
2.葉鎮源(2002)。文件自動化摘要方法之研究及其在中文文件的應用(碩士論文)。國立交通大學，新竹。  延伸查詢new window
圖書
1.Carrington. P. J.、Scott, J.、Wasserman, S.(2005)。Models and methods in social network analysis。Cambridge University Press。  new window
2.Haykin, S.O.(2008)。Neural networks and learning machines。New York, NY：Prentice Hall。  new window
3.Mani, I.、Maybury, M. T.(1999)。Advances in automatic text summarization。Cambridge, MA：The MIT Press。  new window
其他
1.Das, D.，Martins, A.F.T.(2007)。A survey on automatic text summarization，http://www.dipanjandas.com/files/summarization.pdf， 2014/03/17。  new window
圖書論文
1.Ji, H.、Lin, W.P.、Gillick, D.、Hakkani-Tur, D.、Grishman, R.(2013)。Open-domain multi-document summarization via information extraction: challenges and prospects。Multi-source, Multilingual Information Extraction and Summarization。Berlin, Heidelberg, Germany：Springer。  new window
2.Nenkova, A.、McKeown, K.(2012)。A survey of text summarization techniques。Mining Text Data。New York, NY：Springer-Verlag。  new window
3.Quillian, M. R.(1968)。Semantic memory。Semantic information processing。Cambridge, MA：MIT Press。  new window
 
 
 
 
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