It’s interesting that the title seems like a mix on Learning to Rank, in which a preferred ordering is learned for relevant ranking of search results...but does

It’s interesting that the title seems like a mix on Learning to Rank, in which a preferred ordering is learned for relevant ranking of search results...but doesn’t mention this anywhere in the article! As noted, you don’t want to do RL if you can avoid it - so I’m wondering if learning decision trees (common in LTR with LambdaMART) can be helpful with summarization to best fit the annotator preferred passages over others. Perhaps the title name is just a coincidence but there is value in exploring, for example, random forests...which are way easier (and faster) than things like RL or CNNs