@inproceedings{gonzalez-etal-2022-source,
    title = "Source-summary Entity Aggregation in Abstractive Summarization",
    author = "Gonz{\'a}lez, Jos{\'e} {\'A}ngel  and
      Louis, Annie  and
      Cheung, Jackie Chi Kit",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.coling-1.526/",
    pages = "6019--6034",
    abstract = "In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, \textit{Celine Dion} and \textit{Justin Bieber} can be referred to by \textit{Canadian singers} or \textit{celebrities}. In this work, we study this phenomenon in the context of summarization, where entities from a source text are generalized in the summary. We call such instances \textit{source-summary entity aggregations}. We categorize these aggregations into two types and analyze them in the Cnn/Dailymail corpus, showing that they are reasonably frequent. We then examine how well three state-of-the-art summarization systems can generate such aggregations within summaries. We also develop techniques to encourage them to generate more aggregations. Our results show that there is significant room for improvement in producing semantically correct aggregations."
}