In order to make the readers get the most informative and representative opinions efficiently among the news comments, this paper proposes a novel news article comments summarization algorithm and then designs an article summarization system, which combines the clustering algorithm with the ranking algorithm.First, it groups comments using the modified BorderFlow clustering algorithm.Second, for each group, it uses the similar PageRank algorithm to score and rank comments, and selects top comments in each cluster as representation.At last, it ranks the selected comments by MMR algorithm and displays the top-K comments as the comments summarization.According to the experimental statics of NDCG and MAP data, the proposed method meets the intuitive sense of readers more.Meanwhile, it shows the better effectiveness and accuracy theoretically.