A couple thoughts: 1. Summarizing entire bodies of text down into "bite-sized" chunks isn't inherently a good thing. It seems the main use case (and at least t

A couple thoughts: 1. Summarizing entire bodies of text down into "bite-sized" chunks isn't inherently a good thing. It seems the main use case (and at least the one suggested in the demo) is to be used for news articles. Now, I'm totally understanding of the fact that not everyone has the time to read every news article, but as it is, only reading part of the article (or more commonly, only reading the headline) is a huge issue with current consumption of content. This attempt to further summarize articles into small, context-less bites seems to be going in the wrong direction. 2. On the demo page, there is a "Fake News Detection" feature. I threw a couple of articles at it and it left me with so many questions I don't even know where to begin. For a few articles, it just gave me a binary "Real:1 , Fake:0" output. For others, it spit out a couple of numbers for stats like "conspiracy", "irony", "bias", "pseudoscience". Why are these the attributes chosen to measure? How are they calculated? Is something like "irony" even meaningful when trying to detect fake news? Viewing the documentation section of the site, there is a small blurb claiming that it uses "custom AI classifiers", "custom machine learning models trained on fake and biased articles", and "database of trusted and biased websites created by our experts" to calculate these numbers. AKA, there is absolutely zero meaningful explanation as to how these numbers are calculated and why they should be trusted. This entire feature is a complete black box, and for all we know, the "database of trusted websites" could be created by Russian spies trying to sow misinformation.