Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imbalanced multi-label classification using multi-task learning with
  extractive summarization   Extractive summarization and imbalanced multi-label classification often
require vast amounts of training data to avoid overfitting. In situations where
training data is expensive to generate, leveraging information between tasks is
an attractive approach to increasing the amount of available information. This
paper employs multi-task training of an extractive summarizer and an RNN-based
classifier to improve summarization and classification accuracy by 50% and 75%,
respectively, relative to RNN baselines. We hypothesize that concatenating
sentence encodings based on document and class context increases
generalizability for highly variable corpuses.

2
The categories of this paper are: math
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex