TWAG: A Topic-Guided Wikipedia Abstract Generator

Fangwei Zhu, Shangqing Tu, Jiaxin Shi, Juanzi Li, Lei Hou, Tong Cui

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.
Anthology ID:
2021.acl-long.356
Volume:
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:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4623–4635
Language:
URL:
https://aclanthology.org/2021.acl-long.356
DOI:
10.18653/v1/2021.acl-long.356
Bibkey:
Cite (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.
Cite (Informal):
TWAG: A Topic-Guided Wikipedia Abstract Generator (Zhu et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.356.pdf
Optional supplementary material:
 2021.acl-long.356.OptionalSupplementaryMaterial.zip
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.356.mp4
Code
 THU-KEG/TWAG
Data
WikiCatSum, WikiSum, Wikipedia Generation
PDF Search Code Optional supplementary material Video