Abstract:
Document recommendation systems for locating relevant literature have mostly relied
on methods developed a decade ago. This is largely due to the lack of a large offline
gold-standard benchmark of relevant documents that cover a variety of research fields
such that newly developed literature search techniques can be compared, improved
and translated into practice. To overcome this bottleneck, we have established the
RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84
countries, who have collectively annotated the relevance of over 180 000 PubMed-listed
articles with regard to their respective seed (input) article/s. The majority of annotations
were contributed by highly experienced, original authors of the seed articles. The
collected data cover 76% of all unique PubMed Medical Subject Headings descriptors.
No systematic biases were observed across different experience levels, research fields
or time spent on annotations. More importantly, annotations of the same document
pairs contributed by different scientists were highly concordant. We further show that
the three representative baseline methods used to generate recommended articles
for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency
and PubMed Related Articles) had similar overall performances. Additionally, we found
that these methods each tend to produce distinct collections of recommended articles,
suggesting that a hybrid method may be required to completely capture all relevant
articles. The established database server located at https://relishdb.ict.griffith.edu.au is
freely available for the downloading of annotation data and the blind testing of new
methods. We expect that this benchmark will be useful for stimulating the development
of new powerful techniques for title and title/abstract-based search engines for relevant
articles in biomedical research.
