2023/02/25 by Zesheng Shi, Zesheng, Shi, Yucheng, Zhou · 3 citations
Computer Science · #Advanced Text Analysis Techniques #Artificial intelligence #Automatic summarization #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Generative grammar #Graph #Information retrieval #Natural Language Processing Techniques #Natural language processing #Theoretical computer science #Topic Modeling #Topic model
paper · pdf · doi:10.48550/arxiv.2302.13106
openalex publication_date 2023/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.