Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boilerplate Removal using a Neural Sequence Labeling Model   The extraction of main content from web pages is an important task for
numerous applications, ranging from usability aspects, like reader views for
news articles in web browsers, to information retrieval or natural language
processing. Existing approaches are lacking as they rely on large amounts of
hand-crafted features for classification. This results in models that are
tailored to a specific distribution of web pages, e.g. from a certain time
frame, but lack in generalization power. We propose a neural sequence labeling
model that does not rely on any hand-crafted features but takes only the HTML
tags and words that appear in a web page as input. This allows us to present a
browser extension which highlights the content of arbitrary web pages directly
within the browser using our model. In addition, we create a new, more current
dataset to show that our model is able to adapt to changes in the structure of
web pages and outperform the state-of-the-art model.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas