Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization

Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li, Hongye Tan


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2021.emnlp-main.331.pdf
Video:
 https://preview.aclanthology.org/naacl-24-ws-corrections/2021.emnlp-main.331.mp4