# Cross-Domain Keyword Extraction with Keyness Patterns

arXiv: https://arxiv.org/abs/2409.18724 · HTML: https://mlsanity.com/paper/2409.18724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 68 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents](https://mlsanity.com/paper/1911.12559/) (2019)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [Capturing Global Informativeness in Open Domain Keyphrase Extraction](https://mlsanity.com/paper/2004.13639/) (2020)
- [Importance Estimation from Multiple Perspectives for Keyphrase Extraction](https://mlsanity.com/paper/2110.09749/) (2021)
- [TNT-KID: Transformer-based Neural Tagger for Keyword Identification](https://mlsanity.com/paper/2003.09166/) (2020)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization](https://mlsanity.com/paper/1306.4886/) (2013)
- [RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation](https://mlsanity.com/paper/1907.06458/) (2019)
- [Keyphrase Extraction with Span-based Feature Representations](https://mlsanity.com/paper/2002.05407/) (2020)
- [Complex Network based Supervised Keyword Extractor](https://mlsanity.com/paper/1909.12009/) (2019)
- [Extending Neural Keyword Extraction with TF-IDF tagset matching](https://mlsanity.com/paper/2102.00472/) (2021)
- [Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction](https://mlsanity.com/paper/2104.08028/) (2021)
- [Learning Feature Representations for Keyphrase Extraction](https://mlsanity.com/paper/1801.01768/) (2018)

## Abstract (arXiv)

Domain dependence and annotation subjectivity pose challenges for supervised keyword extraction. Based on the premises that second-order keyness patterns are existent at the community level and learnable from annotated keyword extraction datasets, this paper proposes a supervised ranking approach to keyword extraction that ranks keywords with keyness patterns consisting of independent features (such as sublanguage domain and term length) and three categories of dependent features -- heuristic features, specificity features, and representavity features. The approach uses two convolutional-neural-network based models to learn keyness patterns from keyword datasets and overcomes annotation subjectivity by training the two models with bootstrap sampling strategy. Experiments demonstrate that the approach not only achieves state-of-the-art performance on ten keyword datasets in general supervised keyword extraction with an average top-10-F-measure of 0.316 , but also robust cross-domain performance with an average top-10-F-measure of 0.346 on four datasets that are excluded in the training process. Such cross-domain robustness is attributed to the fact that community-level keyness patterns are limited in number and temperately independent of language domains, the distinction between independent features and dependent features, and the sampling training strategy that balances excess risk and lack of negative training data.