Related papers: Multiplex Graph Neural Network for Extractive Text Summarization

Multiplex Graph Neural Network for Extractive Text Summarization

URL: http://arxiv.org/abs/2108.12870v1
Date: Sun, 29 Aug 2021 16:11:01 GMT
Title: Multiplex Graph Neural Network for Extractive Text Summarization
Authors: Baoyu Jing, Zeyu You, Tao Yang, Wei Fan and Hanghang Tong
Abstract summary: Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. 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.
Score: 34.185093491514394
License: http://creativecommons.org/licenses/by/4.0/
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 and superiority of our method.

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