1. Belief Propagation for Maximum Coverage on Weighted Bipartite Graph and Application to Text Summarization
- Author
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Kitano, Hiroki and Takeda, Koujin
- Subjects
Computer Science - Computation and Language ,Computer Science - Information Retrieval ,Computer Science - Machine Learning ,Computer Science - Social and Information Networks ,Statistics - Machine Learning - Abstract
We study text summarization from the viewpoint of maximum coverage problem. In graph theory, the task of text summarization is regarded as maximum coverage problem on bipartite graph with weighted nodes. In recent study, belief-propagation based algorithm for maximum coverage on unweighted graph was proposed using the idea of statistical mechanics. We generalize it to weighted graph for text summarization. Then we apply our algorithm to weighted biregular random graph for verification of maximum coverage performance. We also apply it to bipartite graph representing real document in open text dataset, and check the performance of text summarization. As a result, our algorithm exhibits better performance than greedy-type algorithm in some setting of text summarization., Comment: 4 pages, 4 figures
- Published
- 2020
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