Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keyphrase Generation for Scientific Document Retrieval   Sequence-to-sequence models have lead to significant progress in keyphrase
generation, but it remains unknown whether they are reliable enough to be
beneficial for document retrieval. This study provides empirical evidence that
such models can significantly improve retrieval performance, and introduces a
new extrinsic evaluation framework that allows for a better understanding of
the limitations of keyphrase generation models. Using this framework, we point
out and discuss the difficulties encountered with supplementing documents with
-- not present in text -- keyphrases, and generalizing models across domains.
Our code is available at https://github.com/boudinfl/ir-using-kg

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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org