Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Learning for Neural Keyphrase Generation   We study the problem of generating keyphrases that summarize the key points
for a given document. While sequence-to-sequence (seq2seq) models have achieved
remarkable performance on this task (Meng et al., 2017), model training often
relies on large amounts of labeled data, which is only applicable to
resource-rich domains. In this paper, we propose semi-supervised keyphrase
generation methods by leveraging both labeled data and large-scale unlabeled
samples for learning. Two strategies are proposed. First, unlabeled documents
are first tagged with synthetic keyphrases obtained from unsupervised keyphrase
extraction methods or a selflearning algorithm, and then combined with labeled
samples for training. Furthermore, we investigate a multi-task learning
framework to jointly learn to generate keyphrases as well as the titles of the
articles. Experimental results show that our semi-supervised learning-based
methods outperform a state-of-the-art model trained with labeled data only.

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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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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: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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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: comp-gas
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg