Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Controllable Abstractive Summarization   Current models for document summarization disregard user preferences such as
the desired length, style, the entities that the user might be interested in,
or how much of the document the user has already read. We present a neural
summarization model with a simple but effective mechanism to enable users to
specify these high level attributes in order to control the shape of the final
summaries to better suit their needs. With user input, our system can produce
high quality summaries that follow user preferences. Without user input, we set
the control variables automatically. On the full text CNN-Dailymail dataset, we
outperform state of the art abstractive systems (both in terms of F1-ROUGE1
40.38 vs. 39.53 and human evaluation).

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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
18
The categories of this paper are: econ
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The categories of this paper are: stat
0
The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci