  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.
