  We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent
neural network based encoder decoder architectures and we explore its use for
abstractive document summarization. Mem2Mem transfers "memories" via
readable/writable external memory modules that augment both the encoder and
decoder. Our memory regularization compresses an encoded input article into a
more compact set of sentence representations. Most importantly, the memory
compression step performs implicit extraction without labels, sidestepping
issues with suboptimal ground-truth data and exposure bias of hybrid
extractive-abstractive summarization techniques. By allowing the decoder to
read/write over the encoded input memory, the model learns to read salient
information about the input article while keeping track of what has been
generated. Our Mem2Mem approach yields results that are competitive with state
of the art transformer based summarization methods, but with 16 times fewer
parameters
