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We study text summarization from the viewpoint of maximum coverage problem.In graph theory, the task of text summarization is regarded as maximum coverageproblem on bipartite graph with weighted nodes. In recent study,belief-propagation based algorithm for maximum coverage on unweighted graph wasproposed using the idea of statistical mechanics. We generalize it to weightedgraph for text summarization. Then we apply our algorithm to weighted biregularrandom graph for verification of maximum coverage performance. We also apply itto bipartite graph representing real document in open text dataset, and checkthe performance of text summarization. As a result, our algorithm exhibitsbetter performance than greedy-type algorithm in some setting of textsummarization.

Belief Propagation for Maximum Coverage on Weighted Bipartite Graph and Application to Text Summarization