AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how prevalent such issues are, or when and why these issues are likely to arise, remains limited. Focusing on text summarization -- a common NLP task largely overlooked by the responsible AI community -- we examine research and reporting practices in the current literature. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals. We also discuss current evaluation practices and consider how authors discuss the limitations of both prior work and their own work. Overall, we find that relatively few papers engage with possible stakeholders or contexts of use, which limits their consideration of potential downstream adverse impacts or other responsible AI issues. Based on our findings, we make recommendations on concrete practices and research directions.

AI与NLP研究中缺乏对潜在相关利益相关者、背景使用情境以及可能的负面影响等负责任人工智能问题的考虑，尤其在文本摘要任务中。本研究通过分析2020-2022年发表的333篇ACL文选论文，研究并讨论了相关研究和报告实践的局限性，并提出了具体的实践和研究方向建议。

文本摘要研究中的负责任人工智能考虑：当前实践综述