Text summarization improves by focusing on important tokens
TIAO: Token Importance-Aware Policy Optimization for Text Summarization
Computation and Language
Summary
Summarizing text means making it shorter but still keeping the important ideas clear and connected. The paper introduces a new method called TIAO that helps computer models recognize which words in a summary are most important. By paying special attention to these key words during training, the method improves how well summaries make sense and capture the main points. Experiments show that even smaller trained models using this method can perform as well as much larger AI systems like GPT-4. This approach refines how AI learns to summarize by understanding the role of each token in the sentence.
What this means in practice
- •For software development teams: Enhance text summarization features in applications by training models to weigh key words more effectively for better summary quality.
- •For content management teams: Improve automated content summarization for news or reports by integrating token importance-aware training to create coherent and consistent short forms.
Authors
Qixiu Li, Chenlong Bao, Xiang Zhu, Xiaoyong Li, Ruixin Cao, Shukai Chen, Zhenxiong Zhou
Abstract
Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved through reinforcement learning (RL). However, most existing methods apply reward signals directly to undifferentiated token sequences, overlooking the varying importance of individual tokens to word and sentence level quality in summarization. In this paper, we propose Token Importance-Aware Policy Optimization (TIAO), a novel reinforcement learning strategy that explicitly leverages token-importance awareness. Specifically, TIAO identifies core tokens based on token dependency and reweights a trajectory's advantage according to its overall dependencies. Experiments on the real world dataset show that our TIAO achieves highly competitive results, and that a 7B foundation model enhanced by TIAO performs comparably to GPT-4 and GPT-5-nano. Code is available at https://github.com/TechCloud-x/TIAO