@inproceedings{zhu-etal-2021-twag,
title = "{TWAG}: A Topic-Guided {W}ikipedia Abstract Generator",
author = "Zhu, Fangwei and
Tu, Shangqing and
Shi, Jiaxin and
Li, Juanzi and
Hou, Lei and
Cui, Tong",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.356",
doi = "10.18653/v1/2021.acl-long.356",
pages = "4623--4635",
abstract = "Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.",
}
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<abstract>Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.</abstract>
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%0 Conference Proceedings
%T TWAG: A Topic-Guided Wikipedia Abstract Generator
%A Zhu, Fangwei
%A Tu, Shangqing
%A Shi, Jiaxin
%A Li, Juanzi
%A Hou, Lei
%A Cui, Tong
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhu-etal-2021-twag
%X Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.
%R 10.18653/v1/2021.acl-long.356
%U https://aclanthology.org/2021.acl-long.356
%U https://doi.org/10.18653/v1/2021.acl-long.356
%P 4623-4635
Markdown (Informal)
[TWAG: A Topic-Guided Wikipedia Abstract Generator](https://aclanthology.org/2021.acl-long.356) (Zhu et al., ACL-IJCNLP 2021)
ACL
- Fangwei Zhu, Shangqing Tu, Jiaxin Shi, Juanzi Li, Lei Hou, and Tong Cui. 2021. TWAG: A Topic-Guided Wikipedia Abstract Generator. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4623–4635, Online. Association for Computational Linguistics.