Responsible AI Considerations in Text Summarization Research: A Review
  of Current Practices
Yu Lu Liu Meng Cao Su Lin Blodgett Jackie Chi Kit Cheung Alexandra Olteanu Adam Trischler
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.