Before revision: This paper proposes a novel inference algorithm for seq-to-seq models. It corrects beam search step-by-step via the optimal attention distribution to make the generated text attend to source tokens in a controlled way.
After revision: This study develops a calibrated beam-based algorithm with global awareness for neural abstractive summarization, aiming to improve the local optimality problem of the original beam search in a rigorous way. Specifically, a novel global protocol is proposed based on the attention distribution to make the generated text attend to source tokens in a controlled way.