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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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Journal ArticleDOI
Xiaojun Wan1
TL;DR: This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization.
Abstract: In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the "votings" or "recommendations" between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002---DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.

71 citations

Journal ArticleDOI
TL;DR: A novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation and develops an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking), which outperforms previous graph-based models in both generic and query-oriented summarization tasks.
Abstract: In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document–document and document–sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks.

71 citations

Posted Content
Wei Li, Xinyan Xiao1, Jiachen Liu1, Hua Wu1, Haifeng Wang1, Junping Du 
TL;DR: A neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents, to more effectively process multiple input documents and produce abstractive summaries is developed.
Abstract: Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.

69 citations

Proceedings ArticleDOI
TL;DR: A framework for event detection and summary generation in football broadcast video is proposed, which proposes both deterministic and probabilistic approaches to the detection of the plays and an audio-based hierarchical summarization method.
Abstract: We propose a framework for event detection and summary generation in football broadcast video. First, we formulate summarization as a play detection problem, with play being defined as the most basic segment of time during which the ball is being played. Then we propose both deterministic and probabilistic approaches to the detection of the plays. The detected plays are concatenated to generate a compact, time-compressed summary of the original video. Such a summary is complete in the sense that it contains every meaningful action of the underlying game, and it also servers as a much better starting point for higher-level summarization and other analyses than the original video does. Based on the summary, we also propose an audio-based hierarchical summarization method. Experimental results show the proposed methods work very well on consumer grade platforms.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

69 citations


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