The Mutually Reinforced Manifold-Ranking Based Relevance Propagation Model for Query-Focused Multi-Document Summarization is used to summarize into a single document from multiple document sets. The summarization is based on the ranking of the sentence and clusters. It propagates query relevance from the given query to the document sentences by making use of both the relationships among the sentences and the relationships between the given query and the sentences. The sentences in a document set can be grouped into several topic themes with each theme represented by a cluster of highly related sentences. In this system, we develop two new sentence ranking algorithms, namely the reinforcement after relevance propagation (RARP) algorithm and the reinforcement during relevance propagation (RDRP) algorithm. The results also demonstrate that the RDRP algorithm is more effective than the RARP algorithm. These two algorithms also used to perform the reinforcement between the sentence and theme clusters.