Controlled Text Generation of Lung Cancer Descriptions for Small Sample Data

Lung cancer represents one of the most significant malignant tumors in terms of its threat to the health and life of the population, exhibiting the fastest growing incidence and mortality rates. The utilization of natural language processing methodologies for the analysis of lung cancer data can facilitate the detection, diagnosis and treatment of this disease. Given the sensitive nature of patient data and the difficulty in obtaining a substantial quantity of reliable information, the majority of previous studies have utilized publicly accessible datasets on lung cancer. However, publicly available datasets lack detailed descriptions of patients’ symptoms and personal information. Furthermore, the quality and authenticity of the generated text are difficult to ensure, which presents challenges for lung cancer-related research. To address the aforementioned issues, this paper proposes a controlled text generation method for lung cancer symptom descriptions in the context of small sample data. The method involves two key steps: firstly, the small sample dataset is expanded through an unsupervised learning approach, and secondly, compliant texts are generated by a generator. The method was found to be superior to other unsupervised methods in terms of ROUGE value and other indexes through experimental comparison. Furthermore, the generated text was found to be more closely aligned with the symptom descriptions of patients in real cases through manual evaluation, which provides valuable insights for lung cancer and related research.