Open access
Date
2019Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000394636Publication status
publishedExternal links
Book title
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)Pages / Article No.
Publisher
Association for Computational LinguisticsEvent
Organisational unit
03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
Funding
156976 - Vocal tuning and sequencing in songbirds and in humans (SNF)
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ETH Bibliography
yes
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