Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Salience Estimation with Multi-Attention Learning for Abstractive Text
  Summarization   Attention mechanism plays a dominant role in the sequence generation models
and has been used to improve the performance of machine translation and
abstractive text summarization. Different from neural machine translation, in
the task of text summarization, salience estimation for words, phrases or
sentences is a critical component, since the output summary is a distillation
of the input text. Although the typical attention mechanism can conduct text
fragment selection from the input text conditioned on the decoder states, there
is still a gap to conduct direct and effective salience detection. To bring
back direct salience estimation for summarization with neural networks, we
propose a Multi-Attention Learning framework which contains two new attention
learning components for salience estimation: supervised attention learning and
unsupervised attention learning. We regard the attention weights as the
salience information, which means that the semantic units with large attention
value will be more important. The context information obtained based on the
estimated salience is incorporated with the typical attention mechanism in the
decoder to conduct summary generation. Extensive experiments on some benchmark
datasets in different languages demonstrate the effectiveness of the proposed
framework for the task of abstractive summarization.

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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: physics