Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Global-aware Beam Search for Neural Abstractive Summarization   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.

16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph