Abstract
Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.- Anthology ID:
- 2021.emnlp-main.331
- 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:
- 4045–4052
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.331
- DOI:
- 10.18653/v1/2021.emnlp-main.331
- Cite (ACL):
- Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li, and Hongye Tan. 2021. Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4045–4052, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (Guan et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.emnlp-main.331.pdf