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) within the documents to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity and natural connection relationships), nor model intra-sentential relationships (e.g, semantic similarity and 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 effectiveness of our method.- Anthology ID:
- 2021.emnlp-main.11
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 133–139
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.11
- DOI:
- 10.18653/v1/2021.emnlp-main.11
- Cite (ACL):
- Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, and Hanghang Tong. 2021. Multiplex Graph Neural Network for Extractive Text Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 133–139, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Multiplex Graph Neural Network for Extractive Text Summarization (Jing et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.emnlp-main.11.pdf