99% Energy Efficiency: High-Fidelity Text Summarization via Spectral Linear Algebra (No GPUs Required)

High-volume information retrieval requiring 99% less energy than neural approaches is now possible via Spectral Context Pruning (SCP). This non-neural method outperforms BERT-based baselines by 68.6x in speed using only CPU hardware, extracting narrative backbones through spectral graph theory and the Fiedler vector magnitude. SCP models text as a sparse semantic graph and identifies critical sentences by detecting topological "turning points" in the graph Laplacian, eliminating the need for semantic pre-training. Evaluated on 870,521 real-world news articles, the algorithm captures dialectical structures and filters boilerplate noise with >90% precision. These results prove that narrative coherence is an intrinsic geometric property recoverable via matrix decomposition, offering a scalable "Green AI" alternative for sustainable, massive-scale text processing.