Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Helping News Editors Write Better Headlines: A Recommender to Improve
  the Keyword Contents & Shareability of News Headlines   We present a software tool that employs state-of-the-art natural language
processing (NLP) and machine learning techniques to help newspaper editors
compose effective headlines for online publication. The system identifies the
most salient keywords in a news article and ranks them based on both their
overall popularity and their direct relevance to the article. The system also
uses a supervised regression model to identify headlines that are likely to be
widely shared on social media. The user interface is designed to simplify and
speed the editor's decision process on the composition of the headline. As
such, the tool provides an efficient way to combine the benefits of automated
predictors of engagement and search-engine optimization (SEO) with human
judgments of overall headline quality.

30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
3
The categories of this paper are: physics
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas