references.bib

@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",
    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.",
}