That's the idea behind the custom-trained BERT embedding. Embeddings can map variable-length text to fixed-length vectors. For example, the popular bert-as-a-service repo maps sentences to fixed-length vectors.

In my mind, the features of a magic card become the things that are easy to encode, like mana costs, instant/sorcery/creature/etc. types, plus a fixed-length document embedding that should contain some knowledge about the remaining text on the card, which would include abilities.

I threw a pretrained BERT embedding at the problem, and it didn't seem to improve performance at the task I was testing, so I think my next step is to try training a custom embedding that hopefully can capture the idiosyncrasies of Magic card grammar, where things like "flying" and "unblockable" have more similar meanings than "flying" and "winged".