Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Abstractive Text Summarization with History Aggregation   Recent neural sequence to sequence models have provided feasible solutions
for abstractive summarization. However, such models are still hard to tackle
long text dependency in the summarization task. A high-quality summarization
system usually depends on strong encoder which can refine important information
from long input texts so that the decoder can generate salient summaries from
the encoder's memory. In this paper, we propose an aggregation mechanism based
on the Transformer model to address the challenge of long text representation.
Our model can review history information to make encoder hold more memory
capacity. Empirically, we apply our aggregation mechanism to the Transformer
model and experiment on CNN/DailyMail dataset to achieve higher quality
summaries compared to several strong baseline models on the ROUGE metrics.

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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: math
8
The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org