A Deep News Headline Generation Model with REINFORCE Filter
Aobo Xu, Ling Jian · 2023
Generating accurate and concise headlines based on news content can help people filter out interesting content and improve the quality of life. News headline generation, as the sub-application area of text summarization, has many methodological commonalities but with higher requirements for generated text quality, which is more challenging. In this paper, we propose a new model to generate news headline, named RADGen (REIN-FORCE Aided Deep Generator for news headline), which combines a Transformer-based generator with a sentence-selecting filter based on REINFORCE algorithm. We perform experiments and assessments on Chinese news headline dataset, which achieve 25.71, 8.26 and 23.58 for f-score of ROUGE-1, ROUGE-2 and ROUGE-L respectively, to demonstrate the effectiveness of the proposed model. In addition, ablation experiments show the positive roll of reinforcement learning filter in news headline generation.