# A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents

arXiv: https://arxiv.org/abs/2010.11980 · HTML: https://mlsanity.com/paper/2010.11980/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [A New Entity Extraction Method Based on Machine Reading Comprehension](https://mlsanity.com/paper/2108.06444/) (2021)
- [KnowGraph@IITK at SemEval-2021 Task 11: Building KnowledgeGraph for NLP Research](https://mlsanity.com/paper/2104.01619/) (2021)
- [LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition](https://mlsanity.com/paper/2203.03546/) (2022)

## Abstract (arXiv)

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.