@inproceedings{xiao-2023-multi,
title = "Multi-doc Hybrid Summarization via Salient Representation Learning",
author = "Xiao, Min",
editor = "Sitaram, Sunayana and
Beigman Klebanov, Beata and
Williams, Jason D",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-industry.37",
doi = "10.18653/v1/2023.acl-industry.37",
pages = "379--389",
abstract = "Multi-document summarization is gaining more and more attention recently and serves as an invaluable tool to obtain key facts among a large information pool. In this paper, we proposed a multi-document hybrid summarization approach, which simultaneously generates a human-readable summary and extracts corresponding key evidences based on multi-doc inputs. To fulfill that purpose, we crafted a salient representation learning method to induce latent salient features, which are effective for joint evidence extraction and summary generation. In order to train this model, we conducted multi-task learning to optimize a composited loss, constructed over extractive and abstractive sub-components in a hierarchical way. We implemented the system based on a ubiquiotously adopted transformer architecture and conducted experimental studies on multiple datasets across two domains, achieving superior performance over the baselines.",
}
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%0 Conference Proceedings
%T Multi-doc Hybrid Summarization via Salient Representation Learning
%A Xiao, Min
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xiao-2023-multi
%X Multi-document summarization is gaining more and more attention recently and serves as an invaluable tool to obtain key facts among a large information pool. In this paper, we proposed a multi-document hybrid summarization approach, which simultaneously generates a human-readable summary and extracts corresponding key evidences based on multi-doc inputs. To fulfill that purpose, we crafted a salient representation learning method to induce latent salient features, which are effective for joint evidence extraction and summary generation. In order to train this model, we conducted multi-task learning to optimize a composited loss, constructed over extractive and abstractive sub-components in a hierarchical way. We implemented the system based on a ubiquiotously adopted transformer architecture and conducted experimental studies on multiple datasets across two domains, achieving superior performance over the baselines.
%R 10.18653/v1/2023.acl-industry.37
%U https://aclanthology.org/2023.acl-industry.37
%U https://doi.org/10.18653/v1/2023.acl-industry.37
%P 379-389
Markdown (Informal)
[Multi-doc Hybrid Summarization via Salient Representation Learning](https://aclanthology.org/2023.acl-industry.37) (Xiao, ACL 2023)
ACL