@inproceedings{you-etal-2022-joint,
title = "Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization",
author = "You, Jingyi and
Li, Dongyuan and
Kamigaito, Hidetaka and
Funakoshi, Kotaro and
Okumura, Manabu",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.301/",
doi = "10.18653/v1/2022.naacl-main.301",
pages = "4091--4104",
abstract = "Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a \textit{joint learning-based heterogeneous graph attention network for TLS} (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics."
}
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<abstract>Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a joint learning-based heterogeneous graph attention network for TLS (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics.</abstract>
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%0 Conference Proceedings
%T Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization
%A You, Jingyi
%A Li, Dongyuan
%A Kamigaito, Hidetaka
%A Funakoshi, Kotaro
%A Okumura, Manabu
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F you-etal-2022-joint
%X Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a joint learning-based heterogeneous graph attention network for TLS (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics.
%R 10.18653/v1/2022.naacl-main.301
%U https://aclanthology.org/2022.naacl-main.301/
%U https://doi.org/10.18653/v1/2022.naacl-main.301
%P 4091-4104
Markdown (Informal)
[Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization](https://aclanthology.org/2022.naacl-main.301/) (You et al., NAACL 2022)
ACL