Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal Approach for Metadata Extraction from German Scientific
  Publications   Nowadays, metadata information is often given by the authors themselves upon
submission. However, a significant part of already existing research papers
have missing or incomplete metadata information. German scientific papers come
in a large variety of layouts which makes the extraction of metadata a
non-trivial task that requires a precise way to classify the metadata extracted
from the documents. In this paper, we propose a multimodal deep learning
approach for metadata extraction from scientific papers in the German language.
We consider multiple types of input data by combining natural language
processing and image vision processing. This model aims to increase the overall
accuracy of metadata extraction compared to other state-of-the-art approaches.
It enables the utilization of both spatial and contextual features in order to
achieve a more reliable extraction. Our model for this approach was trained on
a dataset consisting of around 8800 documents and is able to obtain an overall
F1-score of 0.923.

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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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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: eess
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-alg