  Identifying the research topics that best describe the scope of a scientific
publication is a crucial task for editors, in particular because the quality of
these annotations determine how effectively users are able to discover the
right content in online libraries. For this reason, Springer Nature, the
world's largest academic book publisher, has traditionally entrusted this task
to their most expert editors. These editors manually analyse all new books,
possibly including hundreds of chapters, and produce a list of the most
relevant topics. Hence, this process has traditionally been very expensive,
time-consuming, and confined to a few senior editors. For these reasons, back
in 2016 we developed Smart Topic Miner (STM), an ontology-driven application
that assists the Springer Nature editorial team in annotating the volumes of
all books covering conference proceedings in Computer Science. Since then STM
has been regularly used by editors in Germany, China, Brazil, India, and Japan,
for a total of about 800 volumes per year. Over the past three years the
initial prototype has iteratively evolved in response to feedback from the
users and evolving requirements. In this paper we present the most recent
version of the tool and describe the evolution of the system over the years,
the key lessons learnt, and the impact on the Springer Nature workflow. In
particular, our solution has drastically reduced the time needed to annotate
proceedings and significantly improved their discoverability, resulting in 9.3
million additional downloads. We also present a user study involving 9 editors,
which yielded excellent results in term of usability, and report an evaluation
of the new topic classifier used by STM, which outperforms previous versions in
recall and F-measure.
