  Extractive text summarization aims at extracting the most representative
sentences from a given document as its summary. To extract a good summary from
a long text document, sentence embedding plays an important role. Recent
studies have leveraged graph neural networks to capture the inter-sentential
relationship (e.g., the discourse graph) to learn contextual sentence
embedding. However, those approaches neither consider multiple types of
inter-sentential relationships (e.g., semantic similarity & natural
connection), nor model intra-sentential relationships (e.g, semantic &
syntactic relationship among words). To address these problems, we propose a
novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model
different types of relationships among sentences and words. Based on Multi-GCN,
we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive
text summarization. Finally, we evaluate the proposed models on the
CNN/DailyMail benchmark dataset to demonstrate the effectiveness of our method.
