  This study develops a calibrated beam-based algorithm with awareness of the
global attention distribution 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 stipulate how a global optimal hypothesis should attend to the
source. A global scoring mechanism is then developed to regulate beam search to
generate summaries in a near-global optimal fashion. This novel design enjoys a
distinctive property, i.e., the global attention distribution could be
predicted before inference, enabling step-wise improvements on the beam search
through the global scoring mechanism. Extensive experiments on nine datasets
show that the global (attention)-aware inference significantly improves
state-of-the-art summarization models even using empirical hyper-parameters.
The algorithm is also proven robust as it remains to generate meaningful texts
with corrupted attention distributions. The codes and a comprehensive set of
examples are available.
