Responsible AI Considerations in Text Summarization Research: A Review of Current Practices (2023)

Abstract: 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.

## introduction text summarization is an important nlp task where the goal is to generate a shorter version of an input text while preserving its main ideas. applications involving text summarization range from storyline generation and sentence compression to meeting notes summarization and email commitment reminders. as their capabilities increase, especially with the emergence of large language models, automatic text summarization systems have seen increasing use-despite the known risks of generating incorrect, biased, or otherwise harmful summaries. ## background & related work assessing summaries. text summarization is a longstanding nlp research topic with a growing number of applications @xcite , particularly with the increased availability of both datasets and neural summarizers @xcite . text summarization systems are often evaluated using string and word matching metrics like rouge @xcite to assess the similarity between references or gold summaries and system generated summaries. these methods often do not correlate well with human judgments, spurring research into developing automatic metrics with higher human correlations @xcite . nevertheless, automatic metrics can obfuscate when systems may or may not work; e.g., are the summaries more likely to leave out a certain type of content? do they work equally well for content by different speakers? when human judgments are included, they often examine intrinsic qualities of the text such as whether the summaries preserve relevant content or are non-redundant, fluent and coherent @xcite , but rarely extrinsic criteria like how the summaries are used in downstream applications @xcite . more recently, a growing emphasis has been placed on ensuring that generated summaries are consistent with the source text, as abstractive systems risk generating so-called "hallucinations," i.e., text that distorts or is unsupported by the source text @xcite @xcite @xcite . related concerns about factuality, accuracy, and coherency have all been bundled under hallucinations, obscuring what issues the authors are after and the range of harms or adverse impacts they can bring about. our work examines how assessment practices might limit the space of responsible ai issues the community considers by possibly obfuscating some issues and foregrounding others. responsible ai and summarization. while a great deal of work on responsible ai issues has emerged for nlp broadly, much less work has addressed summarization specifically. @xcite examine whether a summary reflects the distribution of opinions in source documents. @xcite and dash et al. (2019) consider whether summarization systems fairly represent document authors from different demographic groups, while shandilya et al. (2020) explore readers' perceptions of fairness in summaries, finding that rouge metrics are not well-suited to capturing perceptions of summary fairness. meanwhile, keswani and celis (2021) find that summarization systems produce summaries under-representing already-minoritized language varieties. in our analysis of the summarization literature, we explore to what extent papers acknowledge these existing concerns and aim to uncover issues not previously raised by existing work. meta-analyses in nlp. we draw inspiration from recent work that analyzes research and reporting practices in nlp. blodgett et al. (2020) explore how nlp papers describe "bias," finding that definitions are often vague, vary widely, and may not be wellmatched to accompanying technical approaches, while benotti and blackburn (2022) examine ethical considerations sections in acl 2021 papers, finding that relatively few (∼15%) include such sections, and that some of these (∼20%) do not meaningfully address either benefits or harms of the research. blodgett et al. (2021) examine how four benchmark datasets conceptualize and operationalize stereotyping, while devinney et al. (2022) analyze papers on gender bias in nlp to uncover how gender is theorized, finding that theorizations rarely are made explicit or engage with gender theories beyond nlp. via interviews and a survey, zhou et al. (2022) examine practitioners' assumptions and practices when evaluating natural language generation systems. elsewhere, work has analyzed evaluation practices in natural language generation @xcite @xcite . we draw on these papers in our own investigation of how papers describe the goals of their work, the approaches they take in evaluating progress towards those goals, and the responsible ai issues they may raise. ## methods to understand research practices surrounding how, when, and which rai issues are or should be considered by the text summarization community, we conducted a systematic survey of the summarization literature. to do so, we followed several steps: we first i) gathered a collection of recent text summarization papers to be examined and annotated ( §3.1) and ii) reviewed a small set of text summarization papers published in various venues to explore relevant practices ( §3.2.1). drawing on this exploratory review, iii) we then developed an annotation scheme (detailed in §3.3), which we used to annotate our collections of text summarization papers ( §3.2.2). finally, iv) we analyze the annotations to understand emerging practices ( §3.2.1). ## findings our systematic review surfaced insights related to what the text summarization community has been focusing on ( §4.1), common evaluation practices the community employs ( §4.2), and how the community engages with ethical considerations ( §4.3). ## discussion and recommendations intended use contexts are often not welldescribed. we find that many papers do not specify an intended domain, and few works mention stakeholders, such as existing or intended users, when imagining intended use contexts. even when such stakeholders are mentioned, imagined benefits are quite narrow in scope. recommendations: we encourage practitioners to conceptualize their contributions' intended use context by articulating, as much as possible, relevant stakeholders, intended domains, and potential benefits and adverse impacts to those stakeholders. quality criteria such as bias and fairness are rarely considered. we find that the priority in summarization evaluation is often on information saliency, linguistic properties, and factuality. recommendations: while these quality criteria are important, other criteria (e.g., social bias, usability) might be relevant and of interest to stakeholders. we encourage authors to consider these criteria, clearly define them (which may require grounding them in specific use contexts), and adopt evaluation practices that meaningfully capture them. to this end, we encourage the development of evaluation instruments (e.g., benchmarks or human evaluation protocols), especially those tailored for, or adaptable to, specific use contexts. there is a lack of engagement with limitations and ethical concerns. we find that most papers do not have discussions on their own limitations, ethical considerations, and other related issues. when they do, they often focus on data-related concerns. this practice is not wrong by itself, but could be indicative of a narrow range of ethical concerns practitioners might be aware of. we especially highlight two areas that tend to be overlooked: i) some model failures are rarely conceptualized as ethical concerns; ii) intended use contexts, including stakeholders, are rarely involved in ethical considerations, which prevents authors from imagining potential harm to said stakeholders. recommendations: to better engage with limitations and ethical concerns, we recommend to: 1) reflect explicitly on the conceptualization of their work's intended use context, and of what constitutes a good summary in that context. what assumptions about system capabilities, stakeholders, or intended domains do choices of quality criteria and accompanying evaluations carry @xcite ? what are the implications of these assumptions and choices? 2) engage with prior literature (e.g., @xcite on ethical concerns and real harms to which nlp systems can give rise, such as hate speech, stereotyping, and misinformation. this could help practitioners critically reflect on their own work and more clearly engage with issues that have already been recognized as ethical concerns, such as "bias" @xcite . 3) engage with ethical issues the text summarization community has already recognized. through our survey we identified issues, such as the risk of misgendering stakeholders in summarization, which have already been pointed out by some members of the community e.g., @xcite . engaging with these issues could also help practitioners imagine limitations and ethical concerns of their own work. ## conclusion we surveyed 333 recent text summarization papers from the acl anthology to examine how the summarization community currently conceptualizes and engages with broader responsible ai issues, and discuss how this might be impacted by existing research practices. while we are heartened by some of the practices we observed, such as evaluating issues like factuality, there remains significant opportunity to also foreground other responsible ai concerns. we hope that, by highlighting current practices and offering actionable guidance, this work will encourage a reflective, collective research and reporting practice in summarization research and beyond.