Intelligent Job Title Generation with Pipeline and Seq2Seq Approaches

Text generation is a crucial and challenging subfield of natural language processing (NLP). Using the practical demand of generating job titles for a large online recruitment platform, the authors explore two main approaches: a rule‑based Pipeline method and a neural Seq2Seq method.

Pipeline method : The process follows three steps—material extraction, material organization, and planning. Material extraction gathers relevant information from job postings (e.g., position, salary, benefits) while handling user‑specific preferences. Statistical techniques such as TF‑IDF, word‑pair probability, and combined cohesion‑freedom metrics are used to build a material library of tens of thousands of points. After extraction, materials are clustered with K‑means based on cosine similarity of word vectors, then ranked using TF‑IDF and BM25‑based TextRank. Finally, the ranked core points are compressed, prefixed with recruitment cues, and ordered to form concise titles.

Seq2Seq method : Recognizing the limitations of the multi‑step pipeline, the authors adopt neural models. They start with a BiLSTM‑Attention baseline, then improve it with a Pointer‑Generator Network that learns a generation probability (P gen ) to balance copying from the source and generating new words, reducing hallucinations. Coverage Attention is added to avoid repeated phrases. Further enhancements include a Field‑Gate that injects structured field information (e.g., job category) into the LSTM cell, and a Dual‑Attention mechanism that jointly attends to global and local contexts, enabling more accurate and diverse title generation.

Evaluation : Title generation is evaluated using ROUGE metrics, similar to text summarization. Experiments on public CNN/Daily Mail data and internal recruitment datasets show that the improved models outperform the baseline by 10‑13% in ROUGE scores, achieve a 78% usable‑title rate, and increase click‑through and application rates by roughly 7% and 5% respectively when deployed online.

Conclusion and outlook : The system has been successfully tested and deployed, demonstrating the practical value of advanced NLP techniques in recruitment. Future work will continue to explore text generation innovations and expand applications to further empower HR services.