Multi-granularity adaptive extractive document summarization with heterogeneous graph neural networks.

ABSTRACT: The crucial aspect of extractive document summarization lies in understanding the interrelations between sentences. Documents inherently comprise a multitude of sentences, and sentence-level models frequently fail to consider the relationships between distantly-placed sentences, resulting in the omission of significant information in the summary. Moreover, information within documents tends to be distributed sparsely, challenging the efficacy of sentence-level models. In the realm of heterogeneous graph neural networks, it has been observed that semantic nodes with varying levels of granularity encapsulate distinct semantic connections. Initially, the incorporation of edge features into the computation of dynamic graph attention networks is performed to account for node relationships. Subs

SUBMITTER: Su W 

PROVIDER: S-EPMC10773739 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature