Multiplex Graph Neural Network for Extractive Text Summarization

TL;DR

This paper introduces a multiplex graph neural network that models multiple inter- and intra-sentential relationships to improve extractive text summarization, demonstrating effectiveness on a benchmark dataset.

Contribution

It proposes a novel Multi-GCN that jointly models diverse relationships among sentences and words for better summarization.

Findings

Effective on CNN/DailyMail dataset

Outperforms existing methods

Models multiple relationship types

Abstract

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.