To Reuse or Not To Reuse?: A Framework and System for Evaluating Summarized Knowledge
Open accessAbstract
As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.
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This aligns with prior work emphasizing the importance of concise memory structures to avoid overloading the system and context window limitations.
“This aligns with prior work emphasizing the importance of concise memory structures to avoid overloading the system and context window limitations [35, 36, 61].”
From CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents (2026) · cites this paper as [36]
Section 5.1 (p. 16) reports that showing all information at once overwhelmed users, and Section 9 (p. 28) describes a hierarchical overview that lets them explore details selectively. This supports the general idea of structuring information to reduce overload, but the paper addresses human attention in a user interface, not concise memory structures, system overload, or context-window limits. (LLM, conf=0.82)
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Research works printed but not linked: 7
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- [30][30] Joseph Chee Chang, Nathan Hahn, Adam Perer, and Aniket Kittur. 2019. SearchLens: composing and capturing complex user interests for exploratory search. In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI '19) . Association for Computing Machinery, Marina del Ray, California, 498-509. https://doi.org/10. 1145/3301275.3302321 -
- [53][53] Udo Hahn and Ulrich Reimer. 1999. Knowledge-based text summarization: Salience and generalization operators for knowledge base abstraction. Advances in automatic text summarization (1999), 215-232. Publisher: MIT Press, Cambridge, Mass. -
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- [68][68] Professor of Management and Director at the Institute of Management Georg Von Krogh, Georg von Krogh, Associate Professor in the Faculty of Social Sciences and the Graduate School of International Corporate Strategy Kazuo Ichijo, Kazuo Ichijo, Ikujiro Nonaka, and Professor of Graduate School of International Corporate Strategy at Hitotsubashi University and the Xerox Distinguished Professor in Knowledge at Hass School of Business Ikujiro Nonaka. 2000. Enabling Knowledge Creation: How to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation . Oxford University Press, USA. Google-Books-ID: JVESDAAAQBAJ. -
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Cited by
Sorted by citations
Liu, M. X., Sarkar, A., Negreanu, C., Zorn, B., Williams, J., Toronto, N., & Gordon, A. D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580817 · Cited by 119 · see citing works
Self-citationWigglite: Low-cost Information Collection and Triage
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Self-citationLiu, M. X., Wu, T., Chen, T., Li, F. M., Kittur, A., & Myers, B. A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642149 · Cited by 56 · see citing works
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Understanding How Programmers Can Use Annotations on Documentation
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How Data Scientists Review the Scholarly Literature
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Concept-Annotated Examples for Library Comparison
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Self-citationMastrianni, A., Twede, H., Sarcevic, A., Wander, J. D., Austin-Tse, C. A., Saponas, S., Rehm, H. L., Conard, A. M., & Hall, A. K. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3756326 · Cited by 8 · see citing works
Support for Long-Form Documentation Authoring and Maintenance
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Self-citationGensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning
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Dear Diary, so far so good—The application works perfectly
Sáenz, J. P., & Russis, L. D. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103943
- Authors
- Liu, M. X., Kittur, A., & Myers, B. A.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2021-04-13
- Volume
- 5
- Issue
- CSCW1
- Pages
- 1-35
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W3158751339
- Cited by
- 23
- References
- 114
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 6
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF (CCF-1814826)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- knowledge reuse, sensemaking, trustworthiness, decision visualization, user-generated content, strata, developer interviews
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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