Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Point-less: More Abstractive Summarization with Pointer-Generator
  Networks   The Pointer-Generator architecture has shown to be a big improvement for
abstractive summarization seq2seq models. However, the summaries produced by
this model are largely extractive as over 30% of the generated sentences are
copied from the source text. This work proposes a multihead attention
mechanism, pointer dropout, and two new loss functions to promote more
abstractive summaries while maintaining similar ROUGE scores. Both the
multihead attention and dropout do not improve N-gram novelty, however, the
dropout acts as a regularizer which improves the ROUGE score. The new loss
function achieves significantly higher novel N-grams and sentences, at the cost
of a slightly lower ROUGE score.

3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio