Title: Mem2Mem: Learning to Summarize Long Texts with Memory-to-Memory Transfer. Abstract: We introduce the Mem2Mem mechanism, a conditional memory-to-memory mechanism that can be appended to general sequence-to-sequence frameworks, and demonstrate its effectiveness in improving long text neural abstractive summarization. Mem2Mem seamlessly transfers "memories" via readable/writable external memory modules that augment both the encoder and decoder. By enabling a memory transfer, Mem2Mem uses representations of highly salient input sentences and performs an implicit sentence extraction step. By allowing the decoder to read and write over encoded input memories, the models learn to store information about the input sequence while keeping track of what has been generated by the decoder. We evaluate Mem2Mem on abstractive text summarization and surpass the current state-of-the-art with less model capacity than competing models and with a full end-to-end training setup. To our knowledge, Mem2Mem is the first mechanism that can effectively use and update memory cells filled with different contextual information.