This paper introduces a novel framework called SoftPromptComp (SPC-LLM) to enhance the efficiency and context processing capabilities of Large Language Models (LLMs). The framework combines natural language summarization with soft prompt compression to condense extensive textual information into concise, semantically rich representations. This approach reduces computational overhead while maintaining or even improving the quality of generated content. The methodology involves extracting summaries from long texts and integrating them with dynamically generated soft prompts, which are then optimized to enhance model performance. The integration of soft prompts with summarization techniques allows LLMs to handle lengthy contexts more effectively, improving their adaptability and efficiency across various NLP tasks. The proposed framework is evaluated on multiple datasets, including CNN/Daily Mail for summarization, Stanford Sentiment Treebank for sentiment analysis, AG News for text classification, and SQuAD v2.0 for question answering. Results show significant improvements in processing speed, with reductions of up to 80.1% in processing times for the SQuAD2.0 dataset. These results demonstrate the effectiveness of the framework in enhancing LLM performance without compromising the quality of outputs. The study also highlights the potential for further research in refining soft prompt parameters and exploring the methodology in multilingual and diverse domains. Overall, the research contributes to the ongoing efforts to optimize LLMs for a broader range of applications, offering a scalable and efficient solution for handling extensive textual data.This paper introduces a novel framework called SoftPromptComp (SPC-LLM) to enhance the efficiency and context processing capabilities of Large Language Models (LLMs). The framework combines natural language summarization with soft prompt compression to condense extensive textual information into concise, semantically rich representations. This approach reduces computational overhead while maintaining or even improving the quality of generated content. The methodology involves extracting summaries from long texts and integrating them with dynamically generated soft prompts, which are then optimized to enhance model performance. The integration of soft prompts with summarization techniques allows LLMs to handle lengthy contexts more effectively, improving their adaptability and efficiency across various NLP tasks. The proposed framework is evaluated on multiple datasets, including CNN/Daily Mail for summarization, Stanford Sentiment Treebank for sentiment analysis, AG News for text classification, and SQuAD v2.0 for question answering. Results show significant improvements in processing speed, with reductions of up to 80.1% in processing times for the SQuAD2.0 dataset. These results demonstrate the effectiveness of the framework in enhancing LLM performance without compromising the quality of outputs. The study also highlights the potential for further research in refining soft prompt parameters and exploring the methodology in multilingual and diverse domains. Overall, the research contributes to the ongoing efforts to optimize LLMs for a broader range of applications, offering a scalable and efficient solution for handling extensive textual data.