Is there any clear tutorial for how to use AutoEncoders with text as input
Concerning Encoding, this link helped me a lot. If you try the code in part 'One Hot Encode with scikit-learn', you will get your encoded vectors. You just have to feed it a list of all your tokens. So I have extracted the fields of the register into a list of markups.

As an output of the one hot encoding part, you will get a n dimensional array, you  feed it to the script in part 'Let's build the simplest possible autoencoder' of the AutoEncoder link

In the Input vector you need to put how many classes you have (instead of the value 784) and in our case, you specify the number of columns of the nd array which represent the unique tokens.

Then to compare the predicted output, you have to use decoding in order to compare visually the text that was regenerated.