  Accurately linking news articles to scientific research works is a critical
component in a number of applications, such as measuring the social impact of a
research work and detecting inaccuracies or distortions in science news.
Although the lack of links between news and literature has been a challenge in
these applications, it is a relatively unexplored research problem. In this
paper we designed and evaluated a new approach that consists of (1) augmenting
latest named-entity recognition techniques to extract various metadata, and (2)
designing a new elastic search engine that can facilitate the use of enriched
metadata queries. To evaluate our approach, we constructed two datasets of
paired news articles and research papers: one is used for training models to
extract metadata, and the other for evaluation. Our experiments showed that the
new approach performed significantly better than a baseline approach used by
altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate
the effectiveness of the approach, we also conducted a study on 37,600
health-related press releases published on EurekAlert!, which showed that our
approach was able to identify the corresponding research papers with a top-1
accuracy of at least 0.97.
