Topic Matching Algorithm Based on Multi-feature Fusion of Key Entities and Text Abstracts.

With the rapid popularization of the Internet, the amount of Internet news has increased dramatically. In this case, how to effectively find relevant reports that are more in line with a specific topic has become an urgent problem to be solved. To address this issue, a topic matching algorithm based on the fusion of key entities and text abstracts was proposed in this study. Firstly, the W² NER model was used for named entity recognition to extract key entities using features such as word frequency, TF-IDF, lexical cohesion word-word similarity, and word-sentence similarity. Secondly, the Pegasus model was used for text summarization, and the deep semantic features of news texts were obtained by combining the key entity features with the text summary features using BiLSTM. Next, the cross-attention mechanism was employed to enhance the interaction between the matching news articles by performing feature interaction. Finally, the deep semantic features of the news texts and the text interaction features were fused together to participate in the determination of text topic matching. Comparative experiments were conducted on real data from Sohu, and the results showed that the proposed algorithm achieved similar accuracy and precision compared to other algorithms, while recall and F1 score were improved. [ABSTRACT FROM AUTHOR]