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

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publicationPROCEEDINGS - 2023 IEEE International Conference on Intelligence and Security Informatics (ISI)
PublisherIEEE
Number of pages6
ISBN (Electronic)979-8-3503-3773-0
ISBN (Print)979-8-3503-3774-7
Publication statusPublished - 2023

Conference

Title20th IEEE International Conference on Intelligence and Security Informatics (ISI 2023)
PlaceUnited States
CityCharlotte
Period2 - 3 October 2023

Abstract

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. © 2023 IEEE.

Research Area(s)

  • media reprint, hotspot reprint paragraph, multi-perspective relevance, writing style

Citation Format(s)

Style-Driven Multi-Perspective Relevance Mining Model for Hotspot Reprint Paragraph Prediction. / Wang, Linzi; Qian, Haoda; Li, Qiudan et al.
PROCEEDINGS - 2023 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2023.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review