Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Abstractive and mixed summarization for long-single documents   The lack of diversity in the datasets available for automatic summarization
of documents has meant that the vast majority of neural models for automatic
summarization have been trained with news articles. These datasets are
relatively small, with an average size of about 600 words, and the models
trained with such data sets see their performance limited to short documents.
In order to surmount this problem, this paper uses scientific papers as the
dataset on which different models are trained. These models have been chosen
based on their performance on the CNN/Daily Mail data set, so that the highest
ranked model of each architectural variant is selected. In this work, six
different models are compared, two with an RNN architecture, one with a CNN
architecture, two with a Transformer architecture and one with a Transformer
architecture combined with reinforcement learning. The results from this work
show that those models that use a hierarchical encoder to model the structure
of the document has a better performance than the rest.

8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat