Abstract
Heterogeneous Graph Neural Networks (HeterGNN) have been recently introduced as an emergent approach for extracting document summarization (EDS) by exploiting the cross-relations between words and sentences. However, applying HeterGNN for long documents is still an open research issue. One of the main majors is the lacking of inter-sentence connections. In this regard, this paper exploits how to apply HeterGNN for long documents by building a graph on sentence-level nodes (homogeneous graph) and combine with HeterGNN for capturing the semantic information in terms of both inter and intra-sentence connections. Experiments on two benchmark datasets of long documents such as PubMed and ArXiv show that our method is able to achieve state-of-the-art results in this research field.- Anthology ID:
- 2022.coling-1.512
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5870–5875
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.512
- DOI:
- Cite (ACL):
- Xuan-Dung Doan, Le-Minh Nguyen, and Khac-Hoai Nam Bui. 2022. Multi Graph Neural Network for Extractive Long Document Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5870–5875, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Multi Graph Neural Network for Extractive Long Document Summarization (Doan et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.512.pdf
- Code
- dungdx34/mtgnn-sum