Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Abstractive Tabular Dataset Summarization via Knowledge Base Semantic
  Embeddings   This paper describes an abstractive summarization method for tabular data
which employs a knowledge base semantic embedding to generate the summary.
Assuming the dataset contains descriptive text in headers, columns and/or some
augmenting metadata, the system employs the embedding to recommend a
subject/type for each text segment. Recommendations are aggregated into a small
collection of super types considered to be descriptive of the dataset by
exploiting the hierarchy of types in a pre-specified ontology. Using February
2015 Wikipedia as the knowledge base, and a corresponding DBpedia ontology as
types, we present experimental results on open data taken from several
sources--OpenML, CKAN and data.world--to illustrate the effectiveness of the
approach.

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The categories of this paper are: math-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: physics
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: econ