  News headline generation aims to produce a short sentence to attract readers
to read the news. One news article often contains multiple keyphrases that are
of interest to different users, which can naturally have multiple reasonable
headlines. However, most existing methods focus on the single headline
generation. In this paper, we propose generating multiple headlines with
keyphrases of user interests, whose main idea is to generate multiple
keyphrases of interest to users for the news first, and then generate multiple
keyphrase-relevant headlines. We propose a multi-source Transformer decoder,
which takes three sources as inputs: (a) keyphrase, (b) keyphrase-filtered
article, and (c) original article to generate keyphrase-relevant, high-quality,
and diverse headlines. Furthermore, we propose a simple and effective method to
mine the keyphrases of interest in the news article and build a first
large-scale keyphrase-aware news headline corpus, which contains over 180K
aligned triples of $<$news article, headline, keyphrase$>$. Extensive
experimental comparisons on the real-world dataset show that the proposed
method achieves state-of-the-art results in terms of quality and diversity

Computation and Language