# Controlled Text Generation of Lung Cancer Descriptions for Small Sample Data
## 1 Introduction  
Natural language processing (NLP) [1] represents a crucial research domain within the field of artificial intelligence. Its objective is to develop technologies that can accurately comprehend and process natural language with the assistance of computer systems. The generation of text represents the most significant challenge in the field of natural language processing, largely due to the inherent complexity involved. To illustrate this, in the field of medicine, the ability to accurately identify the names of diseases and drugs is of paramount importance. In order to produce text of the highest quality and utility, computers must have a more profound understanding of the subject matter, which necessitates the utilization of specific techniques when generating articles on related topics. Consequently, there has been a considerable increase in research activity in the field of controlled text generation [2], which ensures the accuracy and consistency of the text with respect to the topic. However, training text generation models often necessitates the use of high-performance graphics processing units (GPUs) or specialized neural network accelerators, which can result in increased hardware costs and energy consumption. Furthermore, a substantial quantity of data are typically necessary for model training. The number of datasets has a significant impact on the efficacy of model training. Insufficient datasets impede the model's capacity to fully discern the underlying patterns and features of the data, limiting its ability to generalize beyond the specifics of the training data. This will result inthe model exhibiting poor generalization capabilities with regard to new and previously unseen data. Lung cancer [3] is a prevalent form of cancer with a significant global impact. The early detection and treatment of lung cancer can prolong life expectancy and increase the likelihood of a cure. Timely and effective nursing care is of paramount importance in ensuring successful recovery from lung cancer. It is therefore imperative that research be conducted into the symptoms of lung cancer [4]. However, the collection of data pertaining to lung cancer is hindered by privacy concerns, and the paucity of available data renders it insufficient to support robust research. To address this issue, a fixed searcher was employed to obtain target text that was similar to the sample data. This was then combined with the original dataset for training purposes. A pre-training model was employed to integrate the data obtained from the search with the original data, forming a training set on a given topic and with a specified starting text input. This process yielded a comprehensive description of lung cancer symptoms. This method reduces the quantity of data necessary for the original text, does not necessitate a high level of computer performance, and ensures the generation of high-quality content. In this study, we propose a method for generating controlled texts describing lung cancer symptoms with limited data samples. The following points summarize the main contributions:  
1. The searcher framework was constructed using the simulated annealing [5; 6] algorithm to extend the dataset. 2. The Simplified Chinese Pretrained Model (CPM) [7] was used to generate high-quality text describing lung cancer symptoms from a small amount of data, reducing computer hardware requirements and the number of model parameters. 3. The text was segmented using the PKUSEG tool [8] to achieve Chinese text segmentation. Additional phrases related to lung cancer were acquired through point-to-point information and information entropy, and specialized lung cancer dictionaries were incorporated to improve word segmentation results. 4. The model generates a written lung cancer account based on specific topics and initial information. By utilizing a dataset of genuine lung cancer descriptions from a tertiary medical facility, the resulting text closely mirrors real-life scenarios and has substantial practical research value.