  Attention-based neural abstractive summarization systems equipped with copy
mechanisms have shown promising results. Despite this success, it has been
noticed that such a system generates a summary by mostly, if not entirely,
copying over phrases, sentences, and sometimes multiple consecutive sentences
from an input paragraph, effectively performing extractive summarization. In
this paper, we verify this behavior using the latest neural abstractive
summarization system - a pointer-generator network. We propose a simple
baseline method that allows us to control the amount of copying without
retraining. Experiments indicate that the method provides a strong baseline for
abstractive systems looking to obtain high ROUGE scores while minimizing
overlap with the source article, substantially reducing the n-gram overlap with
the original article while keeping within 2 points of the original model's
ROUGE score.
