TNT-KID: Transformer-based Neural Tagger for Keyword Identification
This work addresses the need for efficient keyword extraction from large textual data, offering a domain-specific solution with reduced labeling effort, though it appears incremental as it builds on existing transformer methods.
The researchers tackled keyword identification by developing TNT-KID, a transformer-based neural tagger that adapts transformer architecture and leverages domain-specific pretraining to achieve competitive and robust performance across various datasets while requiring less labeled data than top systems.
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.