@inproceedings{xu-zhao-2022-jointly,
title = "Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders",
author = "Xu, Wang and
Zhao, Tiejun",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.180",
doi = "10.18653/v1/2022.findings-naacl.180",
pages = "2340--2350",
abstract = "Abstractive summarization can generate high quality results with the development of the neural network. However, generating factual consistency summaries is a challenging task for abstractive summarization. Recent studies extract the additional information with off-the-shelf tools from the source document as a clue to guide the summary generation, which shows effectiveness to improve the faithfulness. Unlike these work, we present a novel framework based on conditional variational autoencoders, which induces the guidance information and generates the summary equipment with the guidance synchronously. Experiments on XSUM and CNNDM dataset show that our approach can generate relevant and fluent summaries which is more faithful than the existing state-of-the-art approaches, according to multiple factual consistency metrics.",
}
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%0 Conference Proceedings
%T Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders
%A Xu, Wang
%A Zhao, Tiejun
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F xu-zhao-2022-jointly
%X Abstractive summarization can generate high quality results with the development of the neural network. However, generating factual consistency summaries is a challenging task for abstractive summarization. Recent studies extract the additional information with off-the-shelf tools from the source document as a clue to guide the summary generation, which shows effectiveness to improve the faithfulness. Unlike these work, we present a novel framework based on conditional variational autoencoders, which induces the guidance information and generates the summary equipment with the guidance synchronously. Experiments on XSUM and CNNDM dataset show that our approach can generate relevant and fluent summaries which is more faithful than the existing state-of-the-art approaches, according to multiple factual consistency metrics.
%R 10.18653/v1/2022.findings-naacl.180
%U https://aclanthology.org/2022.findings-naacl.180
%U https://doi.org/10.18653/v1/2022.findings-naacl.180
%P 2340-2350
Markdown (Informal)
[Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders](https://aclanthology.org/2022.findings-naacl.180) (Xu & Zhao, Findings 2022)
ACL