@inproceedings{zhang-etal-2018-neural,
title = "Neural Latent Extractive Document Summarization",
author = "Zhang, Xingxing and
Lapata, Mirella and
Wei, Furu and
Zhou, Ming",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1088",
doi = "10.18653/v1/D18-1088",
pages = "779--784",
abstract = "Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.",
}
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<abstract>Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.</abstract>
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%0 Conference Proceedings
%T Neural Latent Extractive Document Summarization
%A Zhang, Xingxing
%A Lapata, Mirella
%A Wei, Furu
%A Zhou, Ming
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhang-etal-2018-neural
%X Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.
%R 10.18653/v1/D18-1088
%U https://aclanthology.org/D18-1088
%U https://doi.org/10.18653/v1/D18-1088
%P 779-784
Markdown (Informal)
[Neural Latent Extractive Document Summarization](https://aclanthology.org/D18-1088) (Zhang et al., EMNLP 2018)
ACL
- Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018. Neural Latent Extractive Document Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 779–784, Brussels, Belgium. Association for Computational Linguistics.