ESC-CoT: Easy-to-Hard Self-Comparative Chain-of-Thought for News Discourse Profiling

News Discourse Profiling is a discourse task that aims to recognize the semantic role of each sentence in an article. Within this task, the model not only requires comprehending the news content but also needs to analyze the logical relation in a discourse, posing a great challenge to traditional deep learning techniques. Recently, large language model (LLM) technology has demonstrated significant potential for enhancing the task of news discourse profiling. These models, characterized by their vast number of parameters, excel in logical reasoning and contextual understanding, allowing them to grasp the intricate details and relationships within the text. When paired with the effective chain-of-thought (CoT) technique, LLMs have shown impressive performance in many complex reasoning tasks. However, the discourse structure significantly differentiates between news, thus it is difficult to analyze all news with a unified fixed CoT. In this paper, We propose an adaptive CoT technique for the News Discourse Profiling task, namely Easy-to-Hard Self-Comparative Chain-of- Thought (ESC-CoT). ESC-CoT formulates the task as a multiple-iteration process, i.e., ESC-CoT handles the easiest part at the current iteration and gradually transfers to more complex parts, which can reduce the risk of early error propagation. To alleviate error propagation along the reasoning chain, ESC-CoT compares the conflicting results from different CoT processes, analyzing the context and meaning of sentences more deeply, thereby improving LLM's reasoning ability and reducing the possibility of error. Experiment results demonstrate that ESC-CoT substantially surpasses the traditional news discourse profiling methods. Compared with state-of-the-art (SOTA) methods on two benchmark datasets, the ESC-CoT technique shows an improvement of 7.8 and 19.6 on F1 score, respectively.