# TNT-KID: Transformer-based Neural Tagger for Keyword Identification

arXiv: https://arxiv.org/abs/2003.09166 · HTML: https://mlsanity.com/paper/2003.09166/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 15 of 15)

- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2110.06651/) (2021)
- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models](https://mlsanity.com/paper/2505.19240/) (2025)
- [Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?](https://mlsanity.com/paper/2202.06650/) (2022)
- [Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding](https://mlsanity.com/paper/2106.04939/) (2021)
- [Extending Neural Keyword Extraction with TF-IDF tagset matching](https://mlsanity.com/paper/2102.00472/) (2021)
- [Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings](https://mlsanity.com/paper/2305.09316/) (2023)
- [Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness](https://mlsanity.com/paper/2211.05031/) (2022)
- [Unsupervised extraction of local and global keywords from a single text](https://mlsanity.com/paper/2307.14005/) (2023)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)
- [SEKE: Specialised Experts for Keyword Extraction](https://mlsanity.com/paper/2412.14087/) (2024)
- [SPAR: Session-based Pipeline for Adaptive Retrieval on Legacy File Systems](https://mlsanity.com/paper/2512.12938/) (2025)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)
- [Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus](https://mlsanity.com/paper/2608.10688/) (2026)

## Abstract (arXiv)

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.