This document describes a method for sentence similarity based text summarization using clusters. It involves preprocessing text, extracting primitives from sentences, linking primitives, computing sentence similarity, merging similarity values, clustering similar sentences, and extracting a representative sentence from each cluster to generate a summary. Key steps include identifying common elements (primitives) between sentences, representing sentences as vectors of primitives, computing similarity based on shared primitives, clustering similar sentences, pruning clusters to remove dissimilar sentences, ranking clusters by importance, and selecting a representative sentence from each cluster for the summary. The goal is to automatically generate a short summary that captures the essential information from a collection of documents or text on the same topic.