The relevance of the study is determined by the continuous growth of textual information in library information systems and the need to ensure fast and meaningful navigation across electronic collections under constrained computational resources. Existing automatic summarization solutions are primarily oriented toward large-scale language models, which limits their practical deployment within local library infrastructures. In this context, the paper aims to develop a resource-efficient method of semantic text reduction that balances the quality of semantic representation with computational feasibility. The proposed approach is based on a hybrid architecture that sequentially combines lexical reduction using word clouds with neural summarization performed by compact models. In addition, a context-oriented evaluation metric is introduced to assess relevance with regard to semantic coherence, structural characteristics, and domain-specific terms significant for the library environment. An experimental study conducted on a corpus of 1178 documents demonstrates that the hybrid approach improves relevance indicators while simultaneously reducing inference time compared to direct neural summarization of the full text. The obtained results confirm the practical applicability of the proposed method for library information systems operating under limited computational infrastructure and its usefulness for navigation and cataloging tasks.
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