# Extending Neural Keyword Extraction with TF-IDF tagset matching

arXiv: https://arxiv.org/abs/2102.00472 · HTML: https://mlsanity.com/paper/2102.00472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [TNT-KID: Transformer-based Neural Tagger for Keyword Identification](https://mlsanity.com/paper/2003.09166/) (2020)
- [Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings](https://mlsanity.com/paper/1910.08840/) (2019)
- [RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation](https://mlsanity.com/paper/1907.06458/) (2019)

## Built on by (most cited first, 3 of 3)

- [Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?](https://mlsanity.com/paper/2202.06650/) (2022)
- [Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian](https://mlsanity.com/paper/2409.10640/) (2024)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)

## Abstract (arXiv)

Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work we develop and evaluate our methods on four novel data sets covering less represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian and Russian). First, we perform evaluation of two supervised neural transformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a baseline TF-IDF based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate to be used as a recommendation system in the media house environment.