Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Cross-Modality Semantic Correlation Learning Model for
  Multimodal Summarization   Multimodal summarization with multimodal output (MSMO) generates a summary
with both textual and visual content. Multimodal news report contains
heterogeneous contents, which makes MSMO nontrivial. Moreover, it is observed
that different modalities of data in the news report correlate hierarchically.
Traditional MSMO methods indistinguishably handle different modalities of data
by learning a representation for the whole data, which is not directly
adaptable to the heterogeneous contents and hierarchical correlation. In this
paper, we propose a hierarchical cross-modality semantic correlation learning
model (HCSCL) to learn the intra- and inter-modal correlation existing in the
multimodal data. HCSCL adopts a graph network to encode the intra-modal
correlation. Then, a hierarchical fusion framework is proposed to learn the
hierarchical correlation between text and images. Furthermore, we construct a
new dataset with relevant image annotation and image object label information
to provide the supervision information for the learning procedure. Extensive
experiments on the dataset show that HCSCL significantly outperforms the
baseline methods in automatic summarization metrics and fine-grained diversity
tests.

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The categories of this paper are: astro-ph
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The categories of this paper are: math
29
The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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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: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin