  With growing amounts of available textual data, development of algorithms
capable of automatic analysis, categorization and summarization of these data
has become a necessity. In this research we present a novel algorithm for
keyword identification, i.e., an extraction of one or multi-word phrases
representing key aspects of a given document, called Transformer-based Neural
Tagger for Keyword IDentification (TNT-KID). By adapting the transformer
architecture for a specific task at hand and leveraging language model
pretraining on a domain specific corpus, the model is capable of overcoming
deficiencies of both supervised and unsupervised state-of-the-art approaches to
keyword extraction by offering competitive and robust performance on a variety
of different datasets while requiring only a fraction of manually labeled data
required by the best performing systems. This study also offers thorough error
analysis with valuable insights into the inner workings of the model and an
ablation study measuring the influence of specific components of the keyword
identification workflow on the overall performance.
