Mapping the Design Space of Human- AI Interaction in Text Summarization (2022)

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.

## introduction in this era of rapid information consumption, access to high-quality summaries, such as online news highlights and research paper abstracts, is increasingly important. however, summarization is difficult for humans, demanding high cognitive load and expertise @xcite . algorithmic approaches can automate summarization but typically underperform humans and require many high-quality human-written summaries for training @xcite . ai systems that involve humans typically constrain their input to data preparation @xcite or final evaluation @xcite as the first or last step in the summarization workflow. how can humans work together with ai to produce better summaries? other text generation tasks, such as machine translation and creative writing, offer inspiration. beyond being data providers and evaluators, humans can generate text with the assistance of ai. for example, humans can revise ai translation (i.e., "post-editing") @xcite or write with ai suggestions @xcite . these interaction techniques may apply to summarization, yet our understanding of the possible human-ai interactions and how to design for them is incomplete. to that end, we conducted two studies to explore: rq1 what are the different ways that humans and ai can interact in text generation? rq2 what are humans' experience and needs with these different types of ai assistance in the context of summarization? for these studies, we took a human-centered approach-focusing on what humans want from ai and how to improve their experience, rather than 1 458 improving ai models. in study 1, we conducted a systematic literature review of 70 papers that involve ai-assisted text generation systems and developed a taxonomy of five different human-ai interactions (figure 1 ), including distinct human actions, controls, workflows, and interface features. in study 2, to explore human experience with these five interactions in the specific context of text summarization, we conducted interviews with 16 users using prototype interfaces that we developed and identified varied user needs regarding efficiency, control, and trust, informing design considerations for ai-assisted text summarization and generation systems. this work contributes: 1) the first known systematic literature review that provides a taxonomy of human-ai interactions for text generation, 2) an interview user study to understand user experience and needs in each interaction, and 3) an outline of the design space for ai-assisted text summarization and broader text generation systems. this research is a formative, initial exploration that first maps the different types of human-ai interaction for text generation and then surfaces user needs and perceptions for each type in the context of human-ai summarization. such formative work is crucial for understanding what users might want and need from systems without biasing them by existing implementations-future researchers can use these findings to design and evaluate new human-ai text summarization systems. ## study 1: systematic literature review of human-ai text generation for the first study, we performed a systematic literature review of human-ai text generation and developed a taxonomy of existing interaction types, which differ by the level of human control over the output, whether the ai iteratively updates its output based on human interaction, and whether human or ai initiates the interaction workflow. this study identifies and synthesizes the types of interaction explored in prior work. while we do not contribute any entirely new human-ai interactions for text generation, our taxonomy provides grounding for our second study-exploring user needs and experience with these interactions-as well as future research and design in this space. future ai/hci researchers can refer to this taxonomy when designing text generation systems or experiments to select the interaction and design elements that best fit their scenario and to compare findings with prior work toward a more formalized understanding of the space. ## study 2: interview study on ai-assisted text summarization we present a user study in which we evaluated interactions in ai-assisted text summarization with the context of text summarization through interviews aided by prototype interfaces. our goal is not to prescribe which is "best" but to achieve a qualitative understanding of user needs with each interaction to inform future research and design. ## discussion & design implications in the taxonomy, we synthesized five human-ai interactions in ai-assisted text generation. through interviews, we surfaced user experience and needs with each interaction technique to inform future research and design. although our user studies focused on summarization specifically, we believe our insights can be used for designing broader text generation systems. we discuss both general and interaction-specific implications as follows. offer the post-editing option regardless of interaction type. in general, humans like to have the "final say" on ai-generated text. even when participants' role was choosing model output, they still wanted the option to edit to ensure quality. as such, future human-ai text generation systems should provide editing options for the final output. ensure customizable timing of ai-assistance in writing. we found unsolicited auto-completion and suggestions could disrupt users' writing experience. future ai-assisted writing systems should allow users to easily turn off or adjust any automatic functions to decide when to receive help from ai. for example, users can press a button on-demand, instead of getting automatic suggestions. align interactive editing with user expectation. while humans in general like editing support such as dictionary or substitution suggestion, participants were more skeptical about dynamic updates in the interactive editing case, as ai may make big changes when they intended to make minor edits. therefore, they desired to adjust the extent of aipredicted updates based on their intention. echoing literature on predictable ai systems @xcite , future interactive editing systems should consider user expectations and empower 8 users to preview ai actions. systems could also model human editing intention, perhaps via action history like number and location of edits, and adapt ai actions accordingly to better serve user goals. support tailoring text to different audiences. another use case for ai-assisted writing is to tailor generated text to different audiences. for example, systems with guiding model output interaction allow humans to specify desired style, audience and use cases and generate customized text. post editing and interactive editing systems can incorporate scaffolds that provide wording and format suggestions tailored for different scenarios. address trade-offs between efficiency and control. guiding models or selecting model outputs are efficient actions, but humans have concerns about the lack of editing freedom and ownership on ai-generated text. systems that leverage human editing power or support human writing with ai grant more flexibility, but require more effort. users' needs in efficiency and control vary based on their goals and context. for summarization, participants felt more responsibility and thus desired more control on editing and generation when working on professional tasks or on texts that were perceived to be important (e.g., government bills). future systems should consider these differences and assign different level of controls to users accordingly. for instance, systems can classify tasks based on importance and automate more when generating texts that are less important, while inviting humans to participate more in the generation and editing process for more important tasks. foster appropriate trust on ai. our findings echo literature that humans can both over-and under-rely on ai systems @xcite . for example, consistent with @xcite , users may view aigenerated text as an authority and be conservative on making edits. others were uncertain if the aigenerated text or suggestion is reliable, especially when working with important text. our findings point to the need for appropriate trust on ai text generation in general. first, systems should support users to understand how the model generates text, so that they can decide whether to rely on it or not. one technique is to allow humans to participate in the model decision process. systems can refer to the guiding model output interaction and allow users to specify preferences and exper-iment with different outputs. systems can also offer explanations to model mechanism, perhaps through visual representations @xcite . second, systems should provide context support. interview participants, regardless of interaction case, had issues working with ai when summarizing government bills, as they were unfamiliar with the format and jargon. to this end, systems should equip users with sufficient context, so that they can effectively evaluate ai suggestions and make decisions accordingly. for example, systems can offer embedded dictionaries, resource search, or user q&a support. limitations. we note a few important limitations in our study. first, we scoped our paper specifically to human-ai interactions for the goal of generating text, while we did not study the needs of humans who develop models, annotate data, or consume final outputs from ai. second, while we performed a formal systematic literature review in study 1, we may have missed some important papers due to our sampling strategies. therefore, our taxonomy of human-ai interaction for text generation might not cover all possible interaction types. third, our interviews were limited in that participants performed only short-term interaction with a hypothetical ai model in predefined scenarios. users may report different experiences when interacting with real ai models in real-world settings for longer periods of time. that said, our study is a formative first step that aims to ground future research. future researchers and developers could design human-ai text summarization systems based on our findings and further evaluate the systems in more realistic settings, with realistic model outputs, and through large-scale experiments. ## conclusion while humans are commonly asked to generate training data or evaluate final model output in text summarization, we draw attention to the potential of collaborative human interaction when working with ai. our study first contributes a taxonomy of five types of human-ai interactions for text generation tasks. we provide insights on user experience and needs around efficiency, control, and trust for the five interactions and design implications, outlining a variety of considerations for researchers, developers, and designers working toward incorporating human users in text generation systems. 9 we would like to include in our analysis: first, the paper needed to directly contribute to the problem space of text-to-text generation tasks. papers about image/video caption generation, speech recognition, and speech-based machine translation were excluded. second, the paper had to contribute an interface, workflow, or user study that involve human-ai collaboration. for this reason, survey papers were excluded. in addition, because our goal was to identify types of human-ai collaboration that were not obvious previously, we intentionally excluded papers that in which the only human-ai collaboration is human evaluation for model generated final results or human generating training data. the first author and two other authors then independently coded a random sample of 150 papers out of the 692 collected papers for inclusion or exclusion. specifically, the first author coded all 150 papers, and the other two researchers coded 75 respectively. they reached a high inter-rater reliability by comparing the last two researchers' coding with the first authors' respectively (average cohen's @xmath0.9), validating the inclusion coding strategy. the first author then coded the remaining 542 papers for inclusion or exclusion. this led to 106 papers that met our inclusion criteria. due to the rapid advancement in natural language generation in the recent ten years @xcite , we believe insights from recent papers would be most relevant to the design and development of future systems and research. therefore, we excluded papers published earlier than 2011. all these procedures resulted in a final collection of 70 papers selected for analysis. finally, we analyzed the 70 papers following the procedures of thematic analysis @xcite , a manual coding method in qualitative research with which researchers iteratively develop themes from qualitative data. the first author conducted two rounds of open thematic coding on the papers, focusing on aspects such as human actions, collaboration goals, and problem domains. during this process, the team discussed, iterated and refined the themes.