Research and implementation of title generation for news text

Abstract

This paper explores the natural language processing technology of news text, especially the algorithm and application of title generation and abstraction. Based on the analysis of the existing principle of automatic title generation and abstraction, the data sets are preprocessed by text cleaning and word quantization according to the objective and accurate features of news text. Then, the Roberta, GPT-2, and T5-pegasus models were evaluated, and the method of model fusion and integration based on the principle of accuracy priority was proposed to improve the accuracy of title generation by 3%. Finally, the news text processing system is constructed based on the optimized model. Experimental results show that the proposed algorithm has high reliability and robustness. This topic has been applied, and the application shows that the results of the news text processing system are accurate and objective, and have high adaptability.