I'm really interested in this general area of generating good candidate queries from documents, but haven't spent much time on it. I haven't seen it in production, and I don't think the topic gets as much attention as it should because intuitively it sounds like a really good idea, so thanks for the paper!

Results for R@1000 look pretty impressive, and I'll check out the project code. Given the high recall and low MRR, using this for the initial recall step with a rerank is definitely worth looking at. if the high recall carries over to your own data and you can rerank those top 1k to increase precision, then you've got something good.