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.
BibTeX
@inproceedings{guan-etal-2021-frame,
title = "Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization",
author = "Guan, Yong and
Guo, Shaoru and
Li, Ru and
Li, Xiaoli and
Tan, Hongye",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.331/",
doi = "10.18653/v1/2021.emnlp-main.331",
pages = "4045--4052"
}