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
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:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.331.pdf