Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the Quality of Short Narratives from Social Media   An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.

2
The categories of this paper are: math
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph