Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Sentence Ordering Based on Constraint Graphs   Sentence ordering aims at arranging a list of sentences in the correct order.
Based on the observation that sentence order at different distances may rely on
different types of information, we devise a new approach based on
multi-granular orders between sentences. These orders form multiple constraint
graphs, which are then encoded by Graph Isomorphism Networks and fused into
sentence representations. Finally, sentence order is determined using the
order-enhanced sentence representations. Our experiments on five benchmark
datasets show that our method outperforms all the existing baselines
significantly, achieving a new state-of-the-art performance. The results
demonstrate the advantage of considering multiple types of order information
and using graph neural networks to integrate sentence content and order
information for the task. Our code is available at
https://github.com/DaoD/ConstraintGraph4NSO.

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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: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
2
The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph