# Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

arXiv: https://arxiv.org/abs/2204.09781 · HTML: https://mlsanity.com/paper/2204.09781/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 102 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 20 of 20)

- [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)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation](https://mlsanity.com/paper/2010.15728/) (2020)
- [Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment](https://mlsanity.com/paper/1709.09587/) (2017)
- [ML-Net: multi-label classification of biomedical texts with deep neural networks](https://mlsanity.com/paper/1811.05475/) (2018)
- [LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation](https://mlsanity.com/paper/2204.08649/) (2022)
- [Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)](https://mlsanity.com/paper/2010.16413/) (2020)
- [Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view](https://mlsanity.com/paper/2008.03397/) (2020)
- [BagBERT: BERT-based bagging-stacking for multi-topic classification](https://mlsanity.com/paper/2111.05808/) (2021)

## Built on by (most cited first, 14 of 14)

- [Benchmarking large language models for biomedical natural language processing applications and recommendations](https://mlsanity.com/paper/2305.16326/) (2023)
- [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://mlsanity.com/paper/2406.07835/) (2024)
- [Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks](https://mlsanity.com/paper/2311.11608/) (2023)
- [Bioformer: an efficient transformer language model for biomedical text mining](https://mlsanity.com/paper/2302.01588/) (2023)
- [MedINST: Meta Dataset of Biomedical Instructions](https://mlsanity.com/paper/2410.13458/) (2024)
- [Benchmarking GPT-5 for biomedical natural language processing](https://mlsanity.com/paper/2509.04462/) (2025)
- [Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation](https://mlsanity.com/paper/2509.04534/) (2025)
- [Infusing clinical knowledge into tokenisers for language models](https://mlsanity.com/paper/2406.14312/) (2024)
- [Comprehensively identifying Long Covid articles with human-in-the-loop machine learning](https://mlsanity.com/paper/2209.08124/) (2022)
- [Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring Solutions](https://mlsanity.com/paper/2503.03261/) (2025)
- [MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events](https://mlsanity.com/paper/2604.15203/) (2026)
- [Minimal Learning Machine for Multi-Label Learning](https://mlsanity.com/paper/2305.05518/) (2023)
- [Advancing Chinese biomedical text mining with community challenges](https://mlsanity.com/paper/2403.04261/) (2024)
- [Scalable Semantic Steering of Embedding Projections](https://mlsanity.com/paper/2607.03978/) (2026)

## Abstract (arXiv)

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.