Weighted archetypal analysis of the multi-element graph for query-focused multi-document summarization |
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| Affiliation: | 1. Department of Computer Science and Engineering, University of Bologna, Cesena, FC 47521, Italy;2. Umpi R&D, Cattolica, RN 47841, Italy;1. Graduate School of Water Resources, Sungkyunkwan University, Suwon 440-746, Republic of Korea;2. School of Civil Engineering, Seoul National University of Science and Technology, Seoul 139-743, Republic of Korea;1. School of IOT Engineering, Jiangnan University, Wuxi 214122, China;2. Department of Electronics and Information Engineering, Chonbuk National University, Jeonju, Jeonbuk 561756, Republic of Korea;1. Badji Mokhtar University, LRS, Annaba, Algeria;2. Badji Mokhtar University, LRI, Annaba, Algeria;3. Université de Lorraine, LORIA, Nancy, France;4. CNRS UMR 7503, Nancy, France;5. Inria Nancy Grand Est, France;1. College of Biomedical Engineering and Instrument Science, Zhejiang University, 310008 Zhou Yiqing Building 510, Zheda road 38#, Hangzhou, Zhejiang, China;2. Department of Information and Communication Engineering, University of Murcia, Spain;1. School of Mathematics and Computer Applications, Thapar University Patiala, Patiala 147004, Punjab, India;2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India |
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| Abstract: | Most existing research on applying the matrix factorization approaches to query-focused multi-document summarization (Q-MDS) explores either soft/hard clustering or low rank approximation methods. We employ a different kind of matrix factorization method, namely weighted archetypal analysis (wAA) to Q-MDS. In query-focused summarization, given a graph representation of a set of sentences weighted by similarity to the given query, positively and/or negatively salient sentences are values on the weighted data set boundary. We choose to use wAA to compute these extreme values, archetypes, and hence to estimate the importance of sentences in target documents set. We investigate the impact of using the multi-element graph model for query focused summarization via wAA. We conducted experiments on the data of document understanding conference (DUC) 2005 and 2006. Experimental results evidence the improvement of the proposed approach over other closely related methods and many of state-of-the-art systems. |
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| Keywords: | Query-focused document summarization Weighted archetypal analysis Multi-element graph Matrix factorization |
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