  Job descriptions are posted on many online channels, including company
websites, job boards or social media platforms. These descriptions are usually
published with varying text for the same job, due to the requirements of each
platform or to target different audiences. However, for the purpose of
automated recruitment and assistance of people working with these texts, it is
helpful to aggregate job postings across platforms and thus detect duplicate
descriptions that refer to the same job. In this work, we propose an approach
for detecting duplicates in job descriptions. We show that combining
overlap-based character similarity with text embedding and keyword matching
methods lead to convincing results. In particular, we show that although no
approach individually achieves satisfying performance, a combination of string
comparison, deep textual embeddings, and the use of curated weighted lookup
lists for specific skills leads to a significant boost in overall performance.
A tool based on our approach is being used in production and feedback from
real-life use confirms our evaluation.
