Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-to-Text Generation with Content Selection and Planning   Recent advances in data-to-text generation have led to the use of large-scale
datasets and neural network models which are trained end-to-end, without
explicitly modeling what to say and in what order. In this work, we present a
neural network architecture which incorporates content selection and planning
without sacrificing end-to-end training. We decompose the generation task into
two stages. Given a corpus of data records (paired with descriptive documents),
we first generate a content plan highlighting which information should be
mentioned and in which order and then generate the document while taking the
content plan into account. Automatic and human-based evaluation experiments
show that our model outperforms strong baselines improving the state-of-the-art
on the recently released RotoWire dataset.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an