Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
By Dingding Wang, Tao Li, Shenghuo Zhu, Chris H. Q. Ding. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008
345 citations recorded by the source corpus.
Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/d2658b49c9cba95ec997a0341fa1ce7a4cb7f557
How researchers see this paper
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In Artificial Intelligence
- Tao Li
- Nijat R. Isazade
- Rasim M. Alguliyev
- Fei Wang
- Shenghuo Zhu
- Chris H. Q. Ding
- Ramiz M. Aliguliyev
- Mahak Gambhir
Citations in the corpus
- Community discovery using nonnegative matrix factorization (2011, background): “The x-axis is the number of iterations, and the y-axis is the NMI value averaged over 100 independent runs F. Wang et al. 0 100 200 300 400 1300 1400 1500 1600 number of iterations averaged function loss (a) Cornell 0 100 200 300 400 1800 1900 2000 2100 2200 number of iterations averaged function…”
- Multiple documents summarization based on evolutionary optimization algorithm (2013, methodology): “Since clustering is the most important unsupervised categorization method, the application of clustering for solving extractive summarization problems appears to be appropriate and natural (Alguliev & Aliguliyev, 2008; Aliguliyev, 2009a, 2009b, 2010; Dunlavy et al., 2007; Wang et al., 2008).”