This implies that the distance between {r org i } and {r out i } might be much larger than that between {r org i } and {r in i } if F RV focused equally on every answer passage word.
like the original GAN, an AGR is composed of a generator and a discriminator: the generator network is trained for generating (from answer passages) fake representations to make it hard for the discriminator network to distinguish these fake representations from the true representations derived from manually created compact-answers.