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Update summarization via Graph-Based Sentence Ranking

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第5期25卷 1162-1174页

作者： Li, Xuan Du, Liang Shen, Yi-Dong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

Due to the fast evolution of the information on the Internet, update summarization has received much attention in recent years. It is to summarize an evolutionary document collection at current time supposing the users have read some related previous documents. In this paper, we propose a graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since this method is NP-hard, we then propose its approximate method, which is polynomial time solvable. Experiments on the TAC 2008 and 2009 benchmark data sets show the effectiveness and efficiency of our method.

关键词： summarization update summarization topic-focused summarization multidocument summarization extraction-based summarization graph-based ranking manifold ranking large-margin constrained ranking novelty quadratically constrained quadratic programming quadratic programming

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Automatic Multi-document summarization Based on Clustering and Nonnegative Matrix Factorization

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IETE TECHNICAL REVIEW 2010年第2期27卷 167-178页

作者： Park, Sun Cha, ByungRea An, Dong Un Chonbuk Natl Univ Div Elect & Informat Engn Chonju South Korea Chonbuk Natl Univ Adv Grad Educ Ctr Jeonbuk Elect & Informat Techno Chonju South Korea

In this paper, a novel summarization method that uses nonnegative matrix factorization (NMF) and the -clus--tering method is introduced to extract meaningful sentences relevant to a given query. The proposed method decomposes a sentence into the linear combination of sparse nonnegative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can improve the quality of document summaries because it can avoid extracting those sentences whose similarities with the query are high but that are meaningless by using the similarity between the query and the semantic features. In addition, the proposed approach uses the clustering method to remove noise and avoid the biased inherent semantics of the documents being reflected in summaries. The method can -ensure the coherence of summaries by using the rank score of sentences with respect to semantic -features. The experimental results demonstrate that the proposed method has better performance than other -methods that use the thesaurus, the latent semantic analysis (LSA), the K-means, and the NMF.

关键词： Clustering multidocument summarization Nonnegative matrix factorization LSA Semantic feature Semantic variable

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A Bridge between Document Management System and Geographical Information System

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2nd International Conference on Signal-Image Technology and Internet-Based Systems

作者： Mahmoudi, Khaoula Faiz, Sami Ecole Super Commun Tunis SUPCOM Unite Rech Imagerie Satellitaire & Applicat Lab URISA Cite Technol CommunRoute Raoued Km 3-5 El Ghazala 2083 Ariana Tunisia Ecole Natl Ingn Tunis LTSIRS Tunis Tunisia

ISBN: (纸本)9783642013492

The power of geographic information system is to help managers make critical decisions they face daily. The ability to make sound decisions relies upon the availability of relevant information. Typically, spatial databases do not contain much information that could support the decision making, Process ill all situations. To extend the available dataset. we propose ail approach for the enrichment of geographical databases (GDB) and especially their semantic component. This enrichment is performed by providing knowledge extracted from web documents to supplement the aspatial data of the GDB. The knowledge extraction process is reached through the generation of condensed representation of the relevant information derived from a web corpus. This process is carried Out it) a distributed fashion that complies with the multi-agents paradigm.

关键词： Geographic Information System Geographic Database multidocument summarization Multi-Agents Systems TextTiling Rhetorical Structure Theory Spatial Relationships

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iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第3期35卷 1451-1462页

作者： Yeh, Jen-Yuan Ke, Hao-Ren Yang, Wei-Pang Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Informat Management Hsinchu 300 Taiwan Natl Chiao Tung Univ Univ Lib Hsinchu 300 Taiwan Natl Dong Hwa Univ Dept Informat Management Hualien 974 Taiwan

Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops in approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： sentence extraction multidocument summarization spreading activation sentence similarity network feature weigh propagation social network analysis

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multidocument summarization:: An added value to clustering in interactive retrieval

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2004年第2期22卷 215-241页

作者： Maña-López, MJ De Buenaga, M Gómez-Hidalgo, JM Univ Vigo Vigo Spain Univ Europea Madrid Escuela Super Politecn Madrid 28670 Spain

A more and more generalized problem in effective information access is the presence in the same corpus of multiple documents that contain similar information. Generally, users may be interested in locating, for a topic addressed by a group of similar documents, one or several particular aspects. This kind of task, called instance or aspectual retrieval, has been explored in several TREC Interactive Tracks. In this article, we propose in addition to the classification capacity of clustering techniques, the possibility of offering a indicative extract about the contents of several sources by means of multidocument summarization techniques. Two kinds of summaries are provided. The first one covers the similarities of each cluster of documents retrieved. The second one shows the particularities of each document with respect to the common topic in the cluster. The document multitopic structure has been used in order to determine similarities and differences of topics in the cluster of documents. The system is independent of document domain and genre. An evaluation of the proposed system with users proves significant improvements in effectiveness. The results of previous experiments that have compared clustering algorithms are also reported.

关键词： experimentation human factors measurement performance multidocument summarization topic segmentation

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Experimental study on fuzzy word memberships for multi-document summarization

Experimental study on fuzzy word memberships for multi-docum...

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6th International Conference on Information, Communications and Signal Processing

作者： Tjhi, William-Chandra Chen, Lihui Nanyang Technol Univ Sch EEE Div Informat Engn Singapore Singapore

ISBN: (纸本)9781424409822

Fuzzy co-clustering (FCC) is a technique that performs simultaneous fuzzy clustering of objects and features. Recently, several FCC algorithms have been proposed to handle clustering of high-dimensional datasets. The success of these FCC efforts is obvious as it results in both document and word clusters with fuzzy memberships. This paper reports our efforts made on multi-document summarization (MDS) using fuzzy co-clustering approach. The word-membershipsare utilized in the MDS, which appear a good alternative interpretation to a document cluster comparing with the conventional frequency-based approaches. We explain the key differences between a summarizer based on memberships approach against the conventional approach and closely investigate on why in principle the fuzzy co-clustering approach has the high potential to outperform the frequency based approaches for MDS. Experiential study on benchmark dataset DUC 2004 shows very promising results, which encourages the further research in the area.

关键词： fuzzy co-clustering fuzzy memberships multidocument summarization

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Multi-document summarization based on concept space

Multi-document summarization based on concept space

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International Conference on Information Technology

作者： Tang, STK Yen, J Yang, CC Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780377249

Capturing relevant information is important in supporting decision making. In this paper, we propose a new summarization method based on cluster analysis, concept space, and statistical approach to extract the essence from a collection of documents. A prototype system has been developed to condense a set of documents into a list of key issues and expands the key issues to form a summary. Cluster analysis and concept space was used as a bridge to connect convergent and divergent processes. Such approach reduces information loss due to vocabulary switching in the summarization process. In the divergent process, it selects the anchored sentences from the original documents to form a summary based on the concept terms generated previously. A user evaluation has been conducted for its usefulness and other performance indices. The results indicate that such approach is promising.

关键词： document handling decision support systems vocabulary pattern clustering decision making multidocument summarization concept space cluster analysis vocabulary switching user evaluation

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Tracking and summarizing news on a daily basis with Columbia's Newsblaster 02

Tracking and summarizing news on a daily basis with Columbia...

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Proceedings of the second international conference on Human Language Technology Research

作者： Kathleen R. McKeown Regina Barzilay David Evans Vasileios Hatzivassiloglou Judith L. Klavans Ani Nenkova Carl Sable Barry Schiffman Sergey Sigelman Columbia University New York N.Y.

Recently, there have been significant advances in several areas of language technology, including clustering, text categorization, and summarization. However, efforts to combine technology from these areas in a practical system for information access have been limited. In this paper, we present Columbia's Newsblaster system for online news summarization. Many of the tools developed at Columbia over the years are combined together to produce a system that crawls the web for news articles, clusters them on specific topics and produces multidocument summaries for each cluster.

关键词： Columbia Newsblaster multidocument summarization topic detection and tracking clustering

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Interactive, text-based summarization of multiple documents

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COMPUTATIONAL INTELLIGENCE 2000年第4期16卷 606-613页

作者： Stein, GC Strzalkowski, T Wise, GB Corp R&D Niskayuna NY 12309 USA

This paper describes a system for the summarization of multiple text-only news-like documents. We address two main issues: clustering of documents in order to find the main topics that should be mentioned in the multidocument summary and organization of the information in order to create a summary that presents the information in a logical way and is easy to read. The system is based on a single-document summarizer that uses text-extraction.

关键词： multidocument summarization topic detection clustering

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