As other commenter said your workflow requires more than what LLMs are currently capable of.

Summarization capability in LLMs is an equation of LLMs capacity for coherence over long conversational scaling operated on by the LLMs ability to navigate and distill internal structural mappings of conceptual & contextual archetype patterns as discrete objects across a continuous ambiguity sheaf.

This technical jargon that boils down to the idea that an llms summarization capability depends on its parameter size and enough vram for context lengths. Higher parameter and less quantized models maintaining more coherence over long conversations/datasets.

While enterprise llms are able to get up to 128k tokens while maintaining some level of coherence, the local models of medium quantization can handle 16-32k reliably. Theoretically 70b could maybe handle around 64k tokens but even thats stretching it.

Then comes the problem of transformer attention. You can't just put a whole books worth of text into an LLMs input and expect it to inspect any part in real detail. For best results you have to chunk it section by section, chapter by chapter.

So local llms may not be what you're looking for. If you are willing to go enterprise then Claude sonnet and deepseek R1 might be good especially if you set up a API interface.