This document summarizes a research paper on applying a multiviewpoint-based similarity measure to hierarchical document clustering. It begins by introducing document clustering and hierarchical clustering. It then discusses traditional similarity measures used for clustering and introduces a new multiviewpoint-based similarity measure (MVS) that uses multiple reference points to more accurately assess similarity. The paper applies MVS to both hierarchical and k-means clustering algorithms and evaluates the accuracy, precision, and recall of the resulting clusters. It finds that hierarchical clustering with MVS achieves better performance than k-means clustering with MVS based on these evaluation metrics.