Towards Automatic Job Description Generation With Capability-Aware Neural Networks

A job description shows the responsibilities of the job position and the skill requirements for the job. An effective job description will help employers to identify the right talents for the job, and give a clear understanding to candidates of what their duties and qualifications for a particular position would be. However, due to the variation in experiences, it is always a challenge for both hiring managers and recruiters to decide what capabilities the job requires and prioritize them accordingly on the job description. Also, tedious and expensive human efforts are usually required to prepare a job description. Therefore, in this paper, we investigate how to automate the process to generate job descriptions with less human intervention. To this end, we propose an end-to-end capability-aware neural job description generation framework, namely Cajon, to facilitate the writing of job description. Specifically, we first propose a novel capability-aware neural topic model to distill the various capability information from the larger-scale recruitment data. Also, an encoder-decoder recurrent neural network is designed for enabling the job description generation. In particular, the capability-aware attention mechanism and copy mechanism are proposed to guide the generation process to ensure the generated job descriptions can comprehensively cover relevant and representative capability requirements for the job. Moreover, we propose a capability-aware policy gradient training algorithm to further enhance the rationality of the generated job description. Finally, extensive experiments on real-world recruitment data clearly show our Cajon framework can help to generate more effective job descriptions in an interpretable way. In particular, our Cajon framework has been deployed in Baidu as an intelligent tool for talent recruitment.