Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A more abstractive summarization model   Pointer-generator network is an extremely popular method of text
summarization. More recent works in this domain still build on top of the
baseline pointer generator by augmenting a content selection phase, or by
decomposing the decoder into a contextual network and a language model.
However, all such models that are based on the pointer-generator base
architecture cannot generate novel words in the summary and mostly copy words
from the source text. In our work, we first thoroughly investigate why the
pointer-generator network is unable to generate novel words, and then address
that by adding an Out-of-vocabulary (OOV) penalty. This enables us to improve
the amount of novelty/abstraction significantly. We use normalized n-gram
novelty scores as a metric for determining the level of abstraction. Moreover,
we also report rouge scores of our model since most summarization models are
evaluated with R-1, R-2, R-L scores.

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The categories of this paper are: alg-geom
2
The categories of this paper are: math
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc