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Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.


Papers
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Proceedings ArticleDOI
06 Nov 2006
TL;DR: A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge, and the experimental results show the effectiveness of the proposed approach in movie review mining and summarization.
Abstract: With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization.

931 citations

Proceedings ArticleDOI
Yihong Gong1, Xin Liu1
01 Sep 2001
TL;DR: This paper proposes two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents, and uses the latent semantic analysis technique to identify semantically important sentences, for summary creations.
Abstract: In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented.

863 citations

Proceedings ArticleDOI
01 Jan 2004
TL;DR: It is argued that the method presented is reliable, predictive and diagnostic, thus improves considerably over the shortcomings of the human evaluation method currently used in the Document Understanding Conference.
Abstract: We present an empirically grounded method for evaluating content selection in summarization. It incorporates the idea that no single best model summary for a collection of documents exists. Our method quantifies the relative importance of facts to be conveyed. We argue that it is reliable, predictive and diagnostic, thus improves considerably over the shortcomings of the human evaluation method currently used in the Document Understanding Conference.

640 citations

Proceedings ArticleDOI
01 Dec 2002
TL;DR: A generic framework of video summarization based on the modeling of viewer's attention is presented, which takes advantage of computational attention models and eliminates the needs of complex heuristic rules inVideo summarization.
Abstract: Automatic generation of video summarization is one of the key techniques in video management and browsing. In this paper, we present a generic framework of video summarization based on the modeling of viewer's attention. Without fully semantic understanding of video content, this framework takes advantage of understanding of video content, this framework takes advantage of computational attention models and eliminates the needs of complex heuristic rules in video summarization. A set of methods of audio-visual attention model features are proposed and presented. The experimental evaluations indicate that the computational attention based approach is an effective alternative to video semantic analysis for video summarization.

602 citations

Journal ArticleDOI
TL;DR: A comprehensive survey of recent text summarization extractive approaches developed in the last decade is presented and the discussion of useful future directions that can help researchers to identify areas where further research is needed are discussed.
Abstract: As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.

581 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202374
2022160
202152
202061
201947
201852