I was playing around with an open source summarizer for creating executive sumamry of a given text. It is a generalization of a solution based on a paper which uses BERT (by Google) to summarize lectures. Basically give it something that is around 2k words and ask it to make it 20% it will come back with sentences it thinks are important which would make it around 400+ words (roughly 20% depending on sentence lengths).
It has multiple use cases like creating a news summary service (something to summarize all the Corona news for instance) or summarize any long text you need to read with a ML algo.
As it has a docker container with the project which gives out a REST API with Flask, I can quickly build that and make it work on fly.io with fly specific instructions on how to do it. I think this will be a good addition to the examples.
From my previous experience, this 3.5 GB container needs a lot of resources (given the ML model it uses). It needs like 2 GB of RAM to run, just a heads up. On the bright side, this translates to doc on how to scale up services for a heavy and useful application. Thanks!
PS: I am not a Machine Learning enthusiast, I had to solve a problem for a side project and basic googling landed me to this project. I even evaluated Meaning Cloud API but this repo was better at summarizing and less cost with virtually no limit on number of calls :).