@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
%A Guan, Yong
%A Guo, Shaoru
%A Li, Ru
%A Li, Xiaoli
%A Tan, Hongye
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F guan-etal-2021-frame
%X 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.
%R 10.18653/v1/2021.emnlp-main.331
%U https://aclanthology.org/2021.emnlp-main.331
%U https://doi.org/10.18653/v1/2021.emnlp-main.331
%P 4045-4052
Markdown (Informal)
[Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization](https://aclanthology.org/2021.emnlp-main.331) (Guan et al., EMNLP 2021)
ACL