[Question] Best choice for Sentence Compression model? 
I'm trying to develop a model that will do "word level extractive summarization" e.g. that it will delete unimportant words or tokens and summarize a document. This is also known as "Sentence Compression" in the NLP community. 

I'm thinking using the BertforTokenClassification module. Will it work with a large dataset, or must it all fit in my VRAM at once? 

In my case, I also have access to a human made abstractive summary of each document - I was wondering if I could do contextual Sentence Compression by having a model do sequence to sequence conversion between the Abstractive Summary and the original document, and extract all words with the highest attention scores. Anyone know if this is a good idea or not? 
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