Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards the Use of Deep Reinforcement Learning with Global Policy For
  Query-based Extractive Summarisation   Supervised approaches for text summarisation suffer from the problem of
mismatch between the target labels/scores of individual sentences and the
evaluation score of the final summary. Reinforcement learning can solve this
problem by providing a learning mechanism that uses the score of the final
summary as a guide to determine the decisions made at the time of selection of
each sentence. In this paper we present a proof-of-concept approach that
applies a policy-gradient algorithm to learn a stochastic policy using an
undiscounted reward. The method has been applied to a policy consisting of a
simple neural network and simple features. The resulting deep reinforcement
learning system is able to learn a global policy and obtain encouraging
results.

24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
19
The categories of this paper are: eess