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 , highquality , 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 keyphraseaware news headline corpus , which contains over 180 K aligned triples of news article , headline , keyphrase . Extensive experimental comparisons on the real-world dataset show that the proposed method achieves state-of-theart results in terms of quality and diversity 1 . News Headline Generation is an under-explored subtask of text summarization ( See et al . , 2017 ; Gehrmann et al . , 2018 ; Zhong et al . , 2019 ) . Unlike text summaries that contain multiple contextrelated sentences to cover the main ideas of a document , news headlines often contain a single short sentence to encourage users to read the news . Since one news article typically contains multiple keyphrases or topics of interest to different users , it is useful to generate multiple headlines covering different keyphrases for the news article . Multiheadline generation aims to generate multiple independent headlines , which allows us to recommend news with different news headlines based on the interests of users . Besides , multi-headline generation can provide multiple hints for human news editors to assist them in writing news headlines . However , most existing methods ( Takase et al . , 2016 ; Ayana et al . , 2016 ; Murao et al . , 2019 ; Colmenares et al . , 2019 ; Zhang et al . , 2018 ) focus on single-headline generation . The headline generation process is treated as an one-to-one mapping ( the input is an article and the output is a headline ) , which trains and tests the models without any additional guiding information or constraints . We argue that this may lead to two problems . Firstly , since it is reasonable to generate multiple headlines for the news , training to generate the single ground-truth might result in a lack of more detailed guidance . Even worse , a single ground-truth without any constraint or guidance is often not enough to measure the quality of the generated headline for model testing . For example , even if a generated headline is considered reasonable by humans , it can get a low score in ROUGE ( Lin , 2004 ) , because it might focus on the keyphrases or aspects that are not consistent with the ground-truth . In this paper , we incorporate the keyphrase information into the headline generation as additional guidance . Unlike one-to-one mapping employed in previous works , we treat the headline generation process as a two-to-one mapping , where the inputs are news articles and keyphrases , and the output is a headline . We propose a keyphrase-aware news multi-headline generation method , which contains two modules : ( a ) Keyphrase Generation Model , which aims to generate multiple keyphrases of interest to users for the news article . ( b ) Keyphrase-Aware Multi-Headline Generation Model , which takes the news article and a keyphrase as input and generates a keyphrase-relevant news headline . For training models , we build a first large-scale news keyphrase-aware headline corpus that contains 180 K aligned triples of news article , headline , keyphrase . As in years past , a lot of the food trends of the year were based on creating perfectly photogenic dishes . An aesthetically pleasing dish , however , does n't mean it will stand the test of time . In fact , it 's not uncommon for food trends to be all the hype one year and die out the next . From broccoli coffee to " bowl food , " here are 10 food trends that you likely wo n't see in 2019 . ... [ 15 sentences with 307 words are abbreviated from here . ] In 2018 , restaurants all over the US decided it was a good idea to place gold foil on everything from ice cream to chicken wings to pizza resulting in an expensive food trend . For example , the Ainsworth in New York City sells $ 1,000 worth of gold covered chicken wings . It seems everyone can agree that this is a food trend that might soon disappear . In this paper , we demonstrate how to enable news headline generation systems to be aware of keyphrases such that the model can generate diverse news headlines in a controlled manner . We also build a first large-scale keyphrase-aware news headline corpus , which is based on mining the keyphrases of users ' interests in news articles with user queries . Moreover , we propose a keyphraseaware news multi-headline generation model that contains a multi-source Transformer decoder with three variants of attention-based fusing mechanisms . Extensive experiments on the real-world dataset show that our approach can generate highquality , keyphrase-relevant , and diverse news headlines , which outperforms many strong baselines .

Challenge: Existing news headline generation models only focus on generating one output even though news articles often have multiple points.
Approach: They propose a multi-source transformer decoder and train it using a new large-scale keyphrase-aware news headline corpus built from a search engine.
Outcome: Their model outperforms strong baselines on their new real-world keyphrase-aware headline generation dataset.