Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ranking Scientific Papers Using Preference Learning   Peer review is the main quality control mechanism in academia. Quality of
scientific work has many dimensions; coupled with the subjective nature of the
reviewing task, this makes final decision making based on the reviews and
scores therein very difficult and time-consuming. To assist with this important
task, we cast it as a paper ranking problem based on peer review texts and
reviewer scores. We introduce a novel, multi-faceted generic evaluation
framework for making final decisions based on peer reviews that takes into
account effectiveness, efficiency and fairness of the evaluated system. We
propose a novel approach to paper ranking based on Gaussian Process Preference
Learning (GPPL) and evaluate it on peer review data from the ACL-2018
conference. Our experiments demonstrate the superiority of our GPPL-based
approach over prior work, while highlighting the importance of using both texts
and review scores for paper ranking during peer review aggregation.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg