Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

Abstract

Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or semantic structures, which is useful to generate more accurate summary. However, modeling a parsing tree for text summarization is not trivial due to its non-linear structure and it is harder to deal with a document that includes multiple sentences and their parsing trees. In this paper, we propose to use a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The selective attention mechanism is used to extract salient information in semantic and structural aspect and generate an abstractive summary. We evaluate our approach on the CNN/Daily Mail text summarization dataset. The experimental results show that the proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.

Video Summarization Generation Network Based on Dynamic Graph Contrastive Learning and Feature Fusion

“…As an important research direction in this field, video summarization has been widely applied in various application scenarios including news, documentaries, surveillance, education, medicine and more. Its main goal is to extract representative frames or shots by analyzing and processing videos and form a short and compact summary video, retaining the key information of the original video as much as possible, so that users can quickly browse and obtain the core information of the video [1][2][3]. According to the type of the generated summaries, video summarization can be generally divided into two types: static video summarization and dynamic video summarization [4].…”