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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
19 Oct 2009
TL;DR: A photo summarization method is developed to select representative photos based on the observation that the most important objects/views are often captured several times, and then the graph structure is analyzed to facilitate importance ranking.
Abstract: We try to address part of the challenge proposed by CeWe. A photo summarization method is developed to select representative photos. Based on the observation that the most important objects/views are often captured several times, we exploit near-duplicate detection techniques to represent a sequence of photo as a graph, and then the graph structure is analyzed to facilitate importance ranking. We focus on summarizing hundreds of photos taken in journeys lasting for one or two weeks. The qualitative and quantitative measurement results demonstrate the effectiveness of the proposed method.

13 citations

Proceedings ArticleDOI
14 Mar 2010
TL;DR: Experimental results on the broadcast news summarization task show that two different training criteria to alleviate the negative effects caused by the imbalanced-data problem, as well as to boost the summarizer's performance are investigated.
Abstract: Many of the existing machine-learning approaches to speech summarization cast important sentence selection as a two-class classification problem and have shown empirical success for a wide variety of summarization tasks However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance On the other hand, training the summarizer by improving the associated classification accuracy does not always lead to better summarization evaluation performance In view of such phenomena, we hence investigate two different training criteria to alleviate the negative effects caused by them, as well as to boost the summarizer's performance One is to learn the classification capability of a summarizer on the basis of the pair-wise ordering information of sentences in a training document according to a degree of importance The other is to train the summarizer by directly maximizing the associated evaluation score Experimental results on the broadcast news summarization task show that these two training criteria can give substantial improvements over the baseline SVM summarization system

13 citations

Proceedings ArticleDOI
16 Mar 2016
TL;DR: In today's world, the daily hustle-bustle does not permit a human being to devote time for manually summarizing various lengthy documents, so an application that will facilitate automated text summarization is needed.
Abstract: In today's world, the daily hustle-bustle does not permit a human being to devote time for manually summarizing various lengthy documents. Hence it is of utmost importance to devise an application that will facilitate automated text summarization. Not only will this application save time but also render higher scope of efficiency. This application will allow the user to automatically summarize relevant information from various sources.

13 citations

Book ChapterDOI
26 Jun 2012
TL;DR: This paper presents a method for extractive multi-document summarization that explores a two-phase clustering approach that aims not only to create simpler and more incisive sentences, but also to make room for the inclusion of relevant content in the summary as much as possible.
Abstract: This paper presents a method for extractive multi-document summarization that explores a two-phase clustering approach. First, sentences are clustered by similarity, and one sentence per cluster is selected, to reduce redundancy. Then, in order to group them according to topics, those sentences are clustered considering the collection of keywords. Additionally, the summarization process further includes a sentence simplification step, which aims not only to create simpler and more incisive sentences, but also to make room for the inclusion of relevant content in the summary as much as possible.

13 citations

Proceedings Article
Po Hu, Cheng Sun1, Longfei Wu, Donghong Ji1, Chong Teng1 
01 Nov 2011
TL;DR: This work proposes a novel summarization approach based on social context that is implemented by first employing the tripartite clustering algorithm to simultaneously discover document context and user context for a specified document.
Abstract: Heavy research has been done in recent years on tasks of traditional summarization. However, social context, which is critical in building high-quality social summarizer for web documents, is usually neglected. To address this issue, we propose a novel summarization approach based on social context. In this approach, social summarization is implemented by first employing the tripartite clustering algorithm to simultaneously discover document context and user context for a specified document. Then sentence relationships intra and inter documents plus intended user communities are taken into account to evaluate the significance of each sentence in different context views. Finally, a few sentences with highest overall scores are selected to form the summary. Experimental results demonstrate the effectiveness of the proposed approach and show the superior performance over several baselines.

13 citations


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