Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output. We introduce copycat, a transformerbased pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries. We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation),

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... the model is capable of deciding where to copy information from. This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems. More importantly, we show that it addresses a well-known limitation of automatic post-editing -overcorrecting translations -and that our novel mechanism for copying source language words improves the results.