Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Content Extraction for Poster Generation of Scientific Papers   The problem of poster generation for scientific papers is under-investigated.
Posters often present the most important information of papers, and the task
can be considered as a special form of document summarization. Previous studies
focus mainly on poster layout and panel composition, while neglecting the
importance of content extraction. Besides, their datasets are not publicly
available, which hinders further research. In this paper, we construct a
benchmark dataset from scratch for this task. Then we propose a three-step
framework to tackle this task and focus on the content extraction step in this
study. To get both textual and visual elements of a poster panel, a neural
extractive model is proposed to extract text, figures and tables of a paper
section simultaneously. We conduct experiments on the dataset and also perform
ablation study. Results demonstrate the efficacy of our proposed model. The
dataset and code will be released.

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The categories of this paper are: nlin
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The categories of this paper are: math
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
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The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ