Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Encode Evolutionary Knowledge for Automatic Commenting Long
  Novels   Static knowledge graph has been incorporated extensively into
sequence-to-sequence framework for text generation. While effectively
representing structured context, static knowledge graph failed to represent
knowledge evolution, which is required in modeling dynamic events. In this
paper, an automatic commenting task is proposed for long novels, which involves
understanding context of more than tens of thousands of words. To model the
dynamic storyline, especially the transitions of the characters and their
relations, Evolutionary Knowledge Graph(EKG) is proposed and learned within a
multi-task framework. Given a specific passage to comment, sequential modeling
is used to incorporate historical and future embedding for context
representation. Further, a graph-to-sequence model is designed to utilize the
EKG for comment generation. Extensive experimental results show that our
EKG-based method is superior to several strong baselines on both automatic and
human evaluations.

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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: adap-org