<p>Text condensation through multi-document summarization creates a compacted version that represents the fundamental components of the material. The process of information reduction through shortening preserves important points from original sources for easier reader comprehension. A novel multi-document text explanation system has been developed that addresses primary difficulties such as text ambiguity, writing style attributes pertaining to coherence maintenance by eliminating the redundancy. Text processing begins with multiple operations for preprocessing that include tokenization, normalization, stop word elimination and stemming to boost document comprehension. The system determines significant information by calculating scores of extracted features including key-phrases with their location and centrality value together with sentence length, the presence of unneeded information and cue words. Hybrid Red Piranha Grey Wolf optimization serves as a coherence and meaning confirmation method for selecting important sentences. The combination of both optimization approaches produces this hybrid method which improves the selection methods. A CRNN-based Hybrid Deep Learning Model analyzes sentences to determine signature enrollment decisions for summarization. Machine learning approaches that work together enable the proper identification of essential sentences, thereby simplifying the summarization steps. The new method accomplished extensive benchmark assessments to demonstrate its superior capacity and speed in summarizing multiple text documents. Research evaluation incorporated precision, recall along with F1-score measurements, ROUGE-1, ROUGE-2 and ROUGE-S score evaluation methods. These results demonstrate successful resistance against overload along with efficient summary generation based on these evaluation measurements.</p>