Mapping the Design Space of Human-AI Interaction in Text Summarization

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


Abstract
Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans’ roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks. We first conducted a systematic literature review of 70 papers, developing a taxonomy of five interactions in AI-assisted text generation and relevant design dimensions. We designed text summarization prototypes for each interaction. We then interviewed 16 users, aided by the prototypes, to understand their expectations, experience, and needs regarding efficiency, control, and trust with AI in text summarization and propose design considerations accordingly.
Anthology ID:
2022.naacl-main.33
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
431–455
Language:
URL:
https://aclanthology.org/2022.naacl-main.33
DOI:
10.18653/v1/2022.naacl-main.33
Bibkey:
Cite (ACL):
Ruijia Cheng, Alison Smith-Renner, Ke Zhang, Joel Tetreault, and Alejandro Jaimes-Larrarte. 2022. Mapping the Design Space of Human-AI Interaction in Text Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 431–455, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Mapping the Design Space of Human-AI Interaction in Text Summarization (Cheng et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.33.pdf