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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 Article
01 Jan 2004
TL;DR: This study proposes and develops a summary-based event detection (SED) technique that first filters less relative sentences or paragraphs from each news story before performing feature- based event detection.
Abstract: Event detection, an important task in organizational environmental scanning, is to identify the onset of new events from streams of news stories. Existing event detection techniques identify whether a news story contains an unseen event generally by comparing the similarity between features of a new news story and past news stories. However, for illustration and comparison purposes, a news story may contain sentences or paragraphs that are not highly relevant to defining its event. The inclusion of such sentences and paragraphs in the similarity comparison by a traditional event detection technique might significantly degrade its detection effectiveness. Therefore, in this study, we propose and develop a summary-based event detection (SED) technique that first filters less relative sentences or paragraphs from each news story before performing feature-based event detection. Using a traditional event detection technique (i.e., INCR) as a performance benchmark, our empirical evaluation results suggest that the proposed SED technique achieve comparable or even better detection effectiveness than its benchmark technique.

7 citations

Proceedings ArticleDOI
23 May 2009
TL;DR: Experimental result shows that the proposed cluster algorithm is efficient, and modified MMR will be used to extract summary sentences.
Abstract: This paper treat the query sentence as common sentence segmented from multi-documents set for multi-document summarization, and mixed it into sentences set, then this paper create efficient cluster algorithm to cluster all the sentences, The clusters which contain the query sentence will be merged to one cluster, and modified MMR will be used to extract summary sentences. Experimental result shows that the proposed cluster algorithm is efficient.

7 citations

Proceedings ArticleDOI
13 Oct 2015
TL;DR: The empirical results on several challenging, unconstrained videos corroborate the potential of the proposed framework for real-world distributed video summarization applications.
Abstract: Video summarization is a fertile topic in multimedia research. While the advent of modern video cameras and several social networking and video sharing websites (like YouTube, Flickr, Facebook) has led to the generation of humongous amounts of redundant video data, video summarization has emerged as an effective methodology to automatically extract a succinct and condensed representation of a given video. The unprecedented increase in the volume of video data necessitates the usage of multiple, independent computers for its storage and processing. In order to understand the overall essence of a video, it is therefore necessary to develop an algorithm which can summarize a video distributed across multiple computers. In this paper, we propose a novel algorithm for distributed video summarization. Our algorithm requires minimal communication among the computers (over which the video is stored) and also enjoys nice theoretical properties. Our empirical results on several challenging, unconstrained videos corroborate the potential of the proposed framework for real-world distributed video summarization applications.

7 citations

01 Aug 2013
TL;DR: This report provides a description of the methods applied in CIST system participating ACL MultiLing 2013, which is based on sentence extraction and hLDA topic model for multilingual multi-document modeling.
Abstract: This report provides a description of the methods applied in CIST system participating ACL MultiLing 2013. Summarization is based on sentence extraction. hLDA topic model is adopted for multilingual multi-document modeling. Various features are combined to evaluate and extract candidate summary sentences.

7 citations

Book ChapterDOI
09 Oct 2015
TL;DR: The model is established based on multi-feature combination to automatically generate summary for the given news article and outperforms all the other systems at NLPCC2015 on the Weibo-oriented Chinese news summarization task.
Abstract: The past several years have witnessed the rapid development of social media services, and the UGCs User Generated Contents have been increased dramatically, such as tweets in Twitter and posts in Sina Weibo. In this paper, we describe our system at NLPCC2015 on the Weibo-oriented Chinese news summarization task. Our model is established based on multi-feature combination to automatically generate summary for the given news article. In our system, we mainly utilize four kinds of features to compute the significance score of a sentence, including term frequency, sentence position, sentence length and the similarity between sentence and news article title, and then the summary sentences are chosen according to the significance score of each sentence from the news article. The evaluation results on Weibo news document sets show that our system is efficient in Weibo-oriented Chinese news summarization and outperforms all the other systems.

7 citations


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