Style-Driven Multi-Perspective Relevance Mining Model for Hotspot Reprint Paragraph Prediction

Accurately predicting hotspot reprint paragraphs can timely provide valuable clues for topic selection, thereby improving the influence of the disseminated content. Most existing works in media reprint analysis focus on mining reprint relationships and reprint patterns. Meanwhile, few works predict the hotspot reprint paragraph from a fine-grained level. The writing style reflects the structure and semantic logic of the article to some extent. Thus, the challenge is to determine how to effectively incorporate writing style features into the semantic analysis while also reasoning deeply about the semantic relevance between sections of the article. This paper proposes a multi-perspective relevance collaborative modeling method called MPRCM-TS. It integrates writing styles of titles into the semantic representations and deeply mines the multi-perspective semantic relevance between the title and paragraphs on the basis of the attention mechanism. Simultaneously, multiple loss functions collaborate to enhance the parameter optimization ability. We evaluate the performance of the proposed model on a real-world dataset, and the experimental results demonstrate the efficacy.