# Learning Job Title Representation from Job Description Aggregation Network

arXiv: https://arxiv.org/abs/2406.08055 · HTML: https://mlsanity.com/paper/2406.08055/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Representing Long-Range Context for Graph Neural Networks with Global Attention](https://mlsanity.com/paper/2201.08821/) (2022)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax](https://mlsanity.com/paper/1902.08564/) (2019)
- [Pytrec_eval: An Extremely Fast Python Interface to trec_eval](https://mlsanity.com/paper/1805.01597/) (2018)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://mlsanity.com/paper/2004.05484/) (2020)
- [Effective Parallel Corpus Mining using Bilingual Sentence Embeddings](https://mlsanity.com/paper/1807.11906/) (2018)
- [Universal Sentence Representation Learning with Conditional Masked Language Model](https://mlsanity.com/paper/2012.14388/) (2020)
- [JobBERT: Understanding Job Titles through Skills](https://mlsanity.com/paper/2109.09605/) (2021)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers](https://mlsanity.com/paper/2307.03539/) (2023)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [Embedding-based Recommender System for Job to Candidate Matching on Scale](https://mlsanity.com/paper/2107.00221/) (2021)
- [Learning Job Titles Similarity from Noisy Skill Labels](https://mlsanity.com/paper/2207.00494/) (2022)
- [Career Path Prediction using Resume Representation Learning and Skill-based Matching](https://mlsanity.com/paper/2310.15636/) (2023)
- [VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain](https://mlsanity.com/paper/2307.16638/) (2023)
- [Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback](https://mlsanity.com/paper/2112.02530/) (2021)
- [Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval](https://mlsanity.com/paper/2302.01626/) (2023)

## Built on by (most cited first, 4 of 4)

- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (2025)
- [Multilingual JobBERT for Cross-Lingual Job Title Matching](https://mlsanity.com/paper/2507.21609/) (2025)
- [Evaluating Modeling Approaches for Experience-Level Classification in Job Description](https://mlsanity.com/paper/2610.04304/) (2026)

## Abstract (arXiv)

Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach.