@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T An Exploration of Post-Editing Effectiveness in Text Summarization
%A Lai, Vivian
%A Smith-Renner, Alison
%A Zhang, Ke
%A Cheng, Ruijia
%A Zhang, Wenjuan
%A Tetreault, Joel
%A Jaimes-Larrarte, Alejandro
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F lai-etal-2022-exploration
%X 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.
%R 10.18653/v1/2022.naacl-main.35
%U https://aclanthology.org/2022.naacl-main.35
%U https://doi.org/10.18653/v1/2022.naacl-main.35
%P 475-493
Markdown (Informal)
[An Exploration of Post-Editing Effectiveness in Text Summarization](https://aclanthology.org/2022.naacl-main.35) (Lai et al., NAACL 2022)
ACL
- Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, and Alejandro Jaimes-Larrarte. 2022. An Exploration of Post-Editing Effectiveness in Text Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 475–493, Seattle, United States. Association for Computational Linguistics.