@inproceedings{nikolov-etal-2019-summary,
title = "Summary Refinement through Denoising",
author = "Nikolov, Nikola I. and
Calmanovici, Alessandro and
Hahnloser, Richard",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1097",
doi = "10.26615/978-954-452-056-4_097",
pages = "837--843",
abstract = "We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.",
}
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%0 Conference Proceedings
%T Summary Refinement through Denoising
%A Nikolov, Nikola I.
%A Calmanovici, Alessandro
%A Hahnloser, Richard
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F nikolov-etal-2019-summary
%X We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.
%R 10.26615/978-954-452-056-4_097
%U https://aclanthology.org/R19-1097
%U https://doi.org/10.26615/978-954-452-056-4_097
%P 837-843
Markdown (Informal)
[Summary Refinement through Denoising](https://aclanthology.org/R19-1097) (Nikolov et al., RANLP 2019)
ACL
- Nikola I. Nikolov, Alessandro Calmanovici, and Richard Hahnloser. 2019. Summary Refinement through Denoising. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 837–843, Varna, Bulgaria. INCOMA Ltd..