  Text summarization condenses a text to a shorter version while retaining the
important informations. Abstractive summarization is a recent development that
generates new phrases, rather than simply copying or rephrasing sentences
within the original text. Recently neural sequence-to-sequence models have
achieved good results in the field of abstractive summarization, which opens
new possibilities and applications for industrial purposes. However, most
practitioners observe that these models still use large parts of the original
text in the output summaries, making them often similar to extractive
frameworks. To address this drawback, we first introduce a new metric to
measure how much of a summary is extracted from the input text. Secondly, we
present a novel method, that relies on a diversity factor in computing the
neural network loss, to improve the diversity of the summaries generated by any
neural abstractive model implementing beam search. Finally, we show that this
method not only makes the system less extractive, but also improves the overall
rouge score of state-of-the-art methods by at least 2 points.
