Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TabLeX: A Benchmark Dataset for Structure and Content Information
  Extraction from Scientific Tables   Information Extraction (IE) from the tables present in scientific articles is
challenging due to complicated tabular representations and complex embedded
text. This paper presents TabLeX, a large-scale benchmark dataset comprising
table images generated from scientific articles. TabLeX consists of two
subsets, one for table structure extraction and the other for table content
extraction. Each table image is accompanied by its corresponding LATEX source
code. To facilitate the development of robust table IE tools, TabLeX contains
images in different aspect ratios and in a variety of fonts. Our analysis sheds
light on the shortcomings of current state-of-the-art table extraction models
and shows that they fail on even simple table images. Towards the end, we
experiment with a transformer-based existing baseline to report performance
scores. In contrast to the static benchmarks, we plan to augment this dataset
with more complex and diverse tables at regular intervals.

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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat