NAACL 2022long17 citations

An Exploration of Post-Editing Effectiveness in Text Summarization

Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes-Larrarte

Abstract

Automatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? In similar text generation tasks (e.g., machine translation), human-AI collaboration in the form of “post-editing” AI-generated text reduces human workload and improves the quality of AI output. Therefore, we explored whether post-editing offers advantages in text summarization. Specifically, we conducted an experiment with 72 participants, comparing post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text. This study sheds valuable insights on when post-editing is useful for text summarization: it helped in some cases (e.g., when participants lacked domain knowledge) but not in others (e.g., when provided summaries include inaccurate information). Participants’ different editing strategies and needs for assistance offer implications for future human-AI summarization systems.

BibTeX
@inproceedings{lai-etal-2022-exploration,
    title = "An Exploration of Post-Editing Effectiveness in Text Summarization",
    author = "Lai, Vivian  and
      Smith-Renner, Alison  and
      Zhang, Ke  and
      Cheng, Ruijia  and
      Zhang, Wenjuan  and
      Tetreault, Joel  and
      Jaimes-Larrarte, Alejandro",
    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.35/",
    doi = "10.18653/v1/2022.naacl-main.35",
    pages = "475--493"
}
An Exploration of Post-Editing Effectiveness in Text Summarization · NAACL 2022