Using sentence transformers for transforming words with word-windows?

I've written an Extractive Summarizer called [CX_DB8](https://github.com/Hellisotherpeople/CX_DB8) which utilizes pretrained word-embedding models to summarize/semantically-search documents. It works at the word, sentence or paragraph level, and supports any pretrained model available with pytorch-transformers or offered via the Flair AI package.

My question is this: Is "sentence-transformers" suitable for training / fine-tuning with say, 10 word sliding word-windows? What about Paragraph sized texts? Are the pretrained models offered here suitable to run word-windows through them without any fine-tuning? What do you think about utilizing these sentence/word-window embeddings with the PageRank algorithm?

The issue names no files, tests, or entry points; begin by reviewing the sentence-transformers training and encoding documentation and existing examples. A useful outcome would require deciding whether word-window and paragraph inputs are supported, whether fine-tuning is needed, and how PageRank integration should be specified.

Written by the indexing model from the issue text.