We introduce a biologically inspired bird-flocking experimental framework for text summarization that identifies the most salient sentences using contextual information, sentence position, and thematic relevance. The bird-flocking-inspired algorithm, combined with large language models (LLMs), generates summaries with greater factual accuracy. The algorithm ensures source faithfulness by preventing the generation of new, unsupported information, thereby mitigating the risk of model hallucination by grounding the summary exclusively in the original text. While large language models (LLMs) achieve remarkable fluency in abstractive summarization, they frequently hallucinate generating plausible but unsupported content. We introduce a bio-inspired bird-flocking framework that addresses this limitation by serving as a preprocessing step for LLM-based summarization. Our method identifies the most salient, source-faithful sentences using contextual information, sentence position, and thematic relevance, providing LLMs with factually grounded input that constrains generation to verified content. Experimental results show that our methodology consistently produces concise and factually corr
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