pith. sign in

arxiv: 2606.24689 · v1 · pith:HEUCNSXAnew · submitted 2026-06-23 · 💻 cs.SE

Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach

Duc S. H. Nguyen , Minh T. Nguyen , Phuong T. Nguyen , Juri Di Rocco , Davide Di Ruscio This is my paper

Pith reviewed 2026-06-25 22:44 UTC · model grok-4.3

classification 💻 cs.SE
keywords automated summarizationsoftware documentationlarge language modelsmulti-agent systemssoftware engineeringrequirements analysistechnical documentationteacher-student architecture
0
0 comments X

The pith

Metagente's Teacher-Student LLM agents produce better summaries of software documents than single-model baselines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Metagente, an LLM-based multi-agent system built to generate concise and accurate summaries of software documentation. It relies on a Teacher-Student architecture in which multiple agents collaborate to improve relevance and precision. Empirical tests on real-world datasets show the system outperforms the baselines it was compared against for requirements analysis and technical documentation tasks. The work positions this approach as a way to automate and streamline summarization in software engineering workflows.

Core claim

Metagente employs a Teacher-Student architecture where multiple LLM agents collaborate to generate concise and accurate summaries of software documentation. An empirical evaluation on real-world datasets demonstrates Metagente's effectiveness in streamlining workflows, outperforming the considered baselines, and provides evidence that it improves summarization for requirements analysis and technical documentation.

What carries the argument

The Teacher-Student multi-agent architecture in which LLM agents collaborate to enhance the relevance and precision of produced summaries.

Load-bearing premise

The Teacher-Student multi-agent collaboration produces meaningfully better summaries than single-model or other baselines.

What would settle it

An independent replication of the evaluation in which human raters score the quality of Metagente summaries against the baselines and find no statistically significant improvement or find lower quality.

read the original abstract

Large Language Models (LLMs) and LLM-based Multi-Agent Systems (MAS) are revolutionizing software engineering (SE) by advancing automation, decision-making, and knowledge processing. Their recent application to SE tasks has already shown promising results. In this paper, we focus on summarization as a key application area. We present Metagente, an LLM-based MAS designed to generate concise and accurate summaries of software documentation. Metagente employs a Teacher-Student architecture where multiple LLM agents collaborate to enhance relevance and precision of produced summaries. An empirical evaluation on real-world datasets demonstrates Metagente's effectiveness in streamlining workflows, outperforming the considered baselines. The evaluation provides evidence that Metagente improves summarization for requirements analysis and technical documentation. Our findings underscore the transformative potential of these technologies in SE, while identifying challenges and future research directions for their seamless integration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper presents Metagente, an LLM-based multi-agent system (MAS) using a Teacher-Student architecture in which multiple agents collaborate to produce concise and accurate summaries of software documentation. The central claim is that an empirical evaluation on real-world datasets shows Metagente outperforms the considered baselines, with evidence that it improves summarization for requirements analysis and technical documentation.

Significance. If the empirical results can be substantiated with transparent metrics, baselines, and controls for LLM variability, the work would provide a concrete demonstration of multi-agent collaboration benefits in a core SE task. The focus on real-world datasets is a strength, but the absence of any reported evaluation protocol, prompts, or quantitative results prevents assessment of whether the architecture itself drives improvement versus prompt engineering or model choice.

major comments (2)
  1. [Abstract] Abstract: the claim that 'an empirical evaluation on real-world datasets demonstrates Metagente's effectiveness ... outperforming the considered baselines' is load-bearing yet unsupported; no datasets, metrics (ROUGE, BERTScore, human criteria), baselines, statistical tests, or controls for LLM stochasticity are described, rendering the outperformance assertion impossible to evaluate.
  2. [Abstract] Abstract / System description: the Teacher-Student MAS is characterized only as 'multiple LLM agents collaborate to enhance relevance and precision' with no specification of agent roles, communication protocol, iteration mechanism, or prompt templates; without these details the architecture cannot be isolated as the source of any improvement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for highlighting the need for greater specificity in the abstract. We will revise the abstract to include key details on the evaluation and architecture while preserving its concise nature. The full paper already contains the supporting sections, but the abstract will be updated to make the claims evaluable at a glance.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'an empirical evaluation on real-world datasets demonstrates Metagente's effectiveness ... outperforming the considered baselines' is load-bearing yet unsupported; no datasets, metrics (ROUGE, BERTScore, human criteria), baselines, statistical tests, or controls for LLM stochasticity are described, rendering the outperformance assertion impossible to evaluate.

    Authors: We agree the abstract is currently too high-level. In the revision we will add a concise sentence specifying the real-world datasets (industry software documentation for requirements and technical docs), metrics (ROUGE-1/2/L, BERTScore, and human ratings on relevance/accuracy), baselines (vanilla LLM summarization and single-agent variants), and note that results are reported as averages over five runs with different seeds to control for stochasticity. These elements are detailed in Sections 4 and 5 of the manuscript; the abstract will now reference them explicitly. revision: yes

  2. Referee: [Abstract] Abstract / System description: the Teacher-Student MAS is characterized only as 'multiple LLM agents collaborate to enhance relevance and precision' with no specification of agent roles, communication protocol, iteration mechanism, or prompt templates; without these details the architecture cannot be isolated as the source of any improvement.

    Authors: We accept that the abstract must better isolate the MAS contribution. The revised abstract will briefly state the Teacher-Student roles (Teacher provides critique and refinement signals; Students generate and iterate summaries), the protocol (three-round iterative feedback), and note that full prompt templates appear in the appendix. This will allow readers to connect the architecture to the reported gains without expanding the abstract length excessively. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on external dataset comparison

full rationale

The paper introduces Metagente as an LLM-based multi-agent system for document summarization and supports its central claim solely via an empirical evaluation on real-world datasets, reporting outperformance over baselines. No equations, parameters, derivations, or mathematical constructions appear in the provided abstract or description. The evaluation is presented as a direct comparison against external benchmarks (datasets and baselines), with no reduction of any 'prediction' or 'result' to a fitted input or self-citation by construction. This matches the default case of a self-contained empirical paper; the absence of any load-bearing derivation chain precludes circularity under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the unexamined premise that multi-agent LLM collaboration improves summarization quality in this domain; no free parameters or invented physical entities are involved.

axioms (1)
  • domain assumption Large Language Models organized in multi-agent systems can produce more relevant and precise summaries than single models or existing baselines.
    This is the core premise invoked by the Teacher-Student architecture and the claim of outperformance.
invented entities (1)
  • Metagente no independent evidence
    purpose: An LLM-based multi-agent system for software document summarization.
    Newly proposed named system whose effectiveness is asserted via the empirical evaluation.

pith-pipeline@v0.9.1-grok · 5691 in / 1207 out tokens · 24807 ms · 2026-06-25T22:44:18.665778+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

300 extracted references · 52 canonical work pages · 1 internal anchor

  1. [1]

    arXiv preprint arXiv:2407.07959 , year=

    Source code summarization in the era of large language models , author=. arXiv preprint arXiv:2407.07959 , year=

    arXiv

  2. [2]

    Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , pages=

    Analyzing the Performance of Large Language Models on Code Summarization , author=. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , pages=

    2024

  3. [3]

    2024 IEEE Conference on Software Testing, Verification and Validation (ICST) , pages=

    The github recent bugs dataset for evaluating llm-based debugging applications , author=. 2024 IEEE Conference on Software Testing, Verification and Validation (ICST) , pages=. 2024 , organization=

    2024

  4. [4]

    Findings of the Association for Computational Linguistics ACL 2024 , pages=

    DebugBench: Evaluating Debugging Capability of Large Language Models , author=. Findings of the Association for Computational Linguistics ACL 2024 , pages=

    2024

  5. [5]

    Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems , pages=

    Chainforge: A visual toolkit for prompt engineering and llm hypothesis testing , author=. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems , pages=

    2024

  6. [6]

    ACM Transactions on Software Engineering and Methodology , year=

    Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries , author=. ACM Transactions on Software Engineering and Methodology , year=

  7. [7]

    Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

    Llm-based code generation method for golang compiler testing , author=. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

  8. [8]

    IEEE Transactions on Software Engineering , year=

    Llm-based test-driven interactive code generation: User study and empirical evaluation , author=. IEEE Transactions on Software Engineering , year=

  9. [9]

    ACM Transactions on Software Engineering and Methodology , year=

    Bias testing and mitigation in llm-based code generation , author=. ACM Transactions on Software Engineering and Methodology , year=

  10. [10]

    Mining domain knowledge from app descriptions , journal =

    Yuzhou Liu and Lei Liu and Huaxiao Liu and Xiaoyu Wang and Hongji Yang , keywords =. Mining domain knowledge from app descriptions , journal =. 2017 , issn =. doi:https://doi.org/10.1016/j.jss.2017.08.024 , url =

    work page doi:10.1016/j.jss.2017.08.024 2017

  11. [11]

    How ReadMe files are structured in open source Java projects , journal =

    Yuyang Liu and Ehsan Noei and Kelly Lyons , keywords =. How ReadMe files are structured in open source Java projects , journal =. 2022 , issn =. doi:https://doi.org/10.1016/j.infsof.2022.106924 , url =

    work page doi:10.1016/j.infsof.2022.106924 2022

  12. [12]

    Journal of Systems and Software , volume =

    Hudson Borges and Marco. Journal of Systems and Software , volume =. 2018 , issn =. doi:https://doi.org/10.1016/j.jss.2018.09.016 , url =

    work page doi:10.1016/j.jss.2018.09.016 2018

  13. [13]

    Doan, Thu T. H. and Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide , title =. Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering , pages =. 2023 , isbn =. doi:10.1145/3593434.3593448 , abstract =

    work page doi:10.1145/3593434.3593448 2023

  14. [14]

    Proceedings of the 39th IEEE/ACM ASE , pages =

    Wang, Luqiao and Zhou, Yangtao and Zhuang, Huiying and Li, Qingshan and Cui, Di and Zhao, Yutong and Wang, Lu , title =. Proceedings of the 39th IEEE/ACM ASE , pages =. 2024 , isbn =. doi:10.1145/3691620.3695291 , abstract =

    work page doi:10.1145/3691620.3695291 2024

  15. [15]

    Large language model based multi-agents: A survey of progress and challenges

    Guo, Taicheng and Chen, Xiuying and Wang, Yaqi and Chang, Ruidi and Pei, Shichao and Chawla, Nitesh V. and Wiest, Olaf and Zhang, Xiangliang , booktitle =. 2024 , month =. doi:10.24963/ijcai.2024/890 , url =

    work page doi:10.24963/ijcai.2024/890 2024

  16. [16]

    , title =

    White, Jules and Fu, Quchen and Hays, Sam and Sandborn, Michael and Olea, Carlos and Gilbert, Henry and Elnashar, Ashraf and Spencer-Smith, Jesse and Schmidt, Douglas C. , title =. Proceedings of the 30th Conference on Pattern Languages of Programs , articleno =. 2023 , isbn =

    2023

  17. [17]

    Agentless: Demystifying LLM-based Software Engineering Agents

    Chunqiu Steven Xia and Yinlin Deng and Soren Dunn and Lingming Zhang , title =. CoRR , year =. doi:10.48550/ARXIV.2407.01489 , eprinttype =. 2407.01489 , timestamp =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2407.01489

  18. [18]

    A survey on large language model based autonomous agents , volume=

    Lei Wang and Chen Ma and Xueyang Feng and Zeyu Zhang and Hao Yang and Jingsen Zhang and Zhiyuan Chen and Jiakai Tang and Xu Chen and Yankai Lin and Wayne Xin Zhao and Zhewei Wei and Jirong Wen , title =. Frontiers Comput. Sci. , volume =. 2024 , url =. doi:10.1007/S11704-024-40231-1 , timestamp =

    work page doi:10.1007/s11704-024-40231-1 2024

  19. [19]

    2025 , publisher =

    He, Junda and Treude, Christoph and Lo, David , title =. 2025 , publisher =. doi:10.1145/3712003 , abstract =

    work page doi:10.1145/3712003 2025

  20. [20]

    Nguyen and Juri

    Phuong T. Nguyen and Juri. Journal of Systems and Software , volume =. 2024 , issn =. doi:https://doi.org/10.1016/j.jss.2024.112059 , url =

    work page doi:10.1016/j.jss.2024.112059 2024

  21. [21]

    2023 , url =

    Ipek Ozkaya , title =. 2023 , url =. doi:10.1109/MS.2023.3248401 , timestamp =

    work page doi:10.1109/ms.2023.3248401 2023

  22. [22]

    Nguyen, Duc S. H. and Truong, Bach G. and Nguyen, Phuong T. and Di Rocco, Juri and Di Ruscio, Davide , title =. Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering , pages =. 2025 , isbn =. doi:10.1145/3696630.3728511 , abstract =

    work page doi:10.1145/3696630.3728511 2025

  23. [23]

    ROUGE : A Package for Automatic Evaluation of Summaries

    Lin, Chin-Yew. ROUGE : A Package for Automatic Evaluation of Summaries. Text Summarization Branches Out. 2004

    2004

  24. [24]

    2021 , isbn =

    Chen, Songqiang and Xie, Xiaoyuan and Yin, Bangguo and Ji, Yuanxiang and Chen, Lin and Xu, Baowen , title =. 2021 , isbn =. doi:10.1145/3324884.3416538 , booktitle =

    work page doi:10.1145/3324884.3416538 2021

  25. [25]

    2022 , isbn =

    Zhang, Ting and Irsan, Ivana Clairine and Thung, Ferdian and Han, DongGyun and Lo, David and Jiang, Lingxiao , title =. 2022 , isbn =. doi:10.1145/3540250.3558934 , pages =

    work page doi:10.1145/3540250.3558934 2022

  26. [26]

    Wiley Series in Probability and Statistics

    Leonard Kaufman and Peter J. Rousseeuw , title =. 1990 , url =. doi:10.1002/9780470316801 , isbn =

    work page doi:10.1002/9780470316801 1990

  27. [27]

    Enhancing Trustability of Android Applications via User-Centric Flexible Permissions , year=

    Scoccia, Gian Luca and Malavolta, Ivano and Autili, Marco and Di Salle, Amleto and Inverardi, Paola , journal=. Enhancing Trustability of Android Applications via User-Centric Flexible Permissions , year=

  28. [28]

    Systematic review on privacy categorisation , journal =

    Paola Inverardi and Patrizio Migliarini and Massimiliano Palmiero , keywords =. Systematic review on privacy categorisation , journal =. 2023 , issn =. doi:https://doi.org/10.1016/j.cosrev.2023.100574 , url =

    work page doi:10.1016/j.cosrev.2023.100574 2023

  29. [29]

    2021 , institution=

    The rise of digital health technologies during the pandemic , author=. 2021 , institution=

    2021

  30. [30]

    , title=

    Lockee, Barbara B. , title=. Nature Electronics , year=. doi:10.1038/s41928-020-00534-0 , url=

    work page doi:10.1038/s41928-020-00534-0

  31. [31]

    HHAI2023 - Volume 368 of Frontiers in Artificial Intelligence and Applications, IOS Press 10.3233/FAIA230092 , pages =

    Costanza Alfieri and Donatella Donati and Simone Gozzano and Lorenzo Greco and Marco Segala , title =. HHAI2023 - Volume 368 of Frontiers in Artificial Intelligence and Applications, IOS Press 10.3233/FAIA230092 , pages =. 2023 , howpublished =

    work page doi:10.3233/faia230092 2023

  32. [32]

    Costanza Alfieri and Paola Inverardi and Patrizio Migliarini and Massimiliano Palmiero , title =. HHAI2022 - Volume 354 of Frontiers in Artificial Intelligence and Applications, IOS Press 10.3233/FAIA220194, ISBN print: 978-1-64368-308-9, ISBN online: 978-1-64368-309-6 , pages =. 2022 , howpublished =

    work page doi:10.3233/faia220194 2022

  33. [33]

    , title = "

    Atherton, Charles R. , title = ". Social Work , volume =. 1976 , month =. doi:10.1093/sw/21.4.338 , url =

    work page doi:10.1093/sw/21.4.338 1976

  34. [34]

    Cranor, Lorrie Faith , title =. Commun. ACM , month =. 2022 , issue_date =. doi:10.1145/3538639 , abstract =

    work page doi:10.1145/3538639 2022

  35. [35]

    Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems , articleno =

    Habib, Hana and Li, Megan and Young, Ellie and Cranor, Lorrie , title =. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems , articleno =. 2022 , isbn =. doi:10.1145/3491102.3501985 , abstract =

    work page doi:10.1145/3491102.3501985 2022

  36. [36]

    Cranor, Lorrie Faith and Habib, Hana , title =. Commun. ACM , month =. 2023 , issue_date =. doi:10.1145/3581764 , abstract =

    work page doi:10.1145/3581764 2023

  37. [37]

    Grundy , title =

    Zhongxin Liu and Xin Xia and David Lo and John C. Grundy , title =. Autom. Softw. Eng. , volume =. 2019 , url =. doi:10.1007/s10515-019-00254-6 , timestamp =

    work page doi:10.1007/s10515-019-00254-6 2019

  38. [38]

    Pattaraporn Sangaroonsilp and Morakot Choetkiertikul and Hoa Khanh Dam and Aditya Ghose , title =. Autom. Softw. Eng. , volume =. 2023 , url =

    2023

  39. [39]

    arXiv preprint: arXiv:2307.03652 , year=

    Systematic Review on Privacy Categorization , author=. arXiv preprint: arXiv:2307.03652 , year=

    arXiv

  40. [40]

    The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review , journal =

    Susanne Barth and Menno D.T. The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review , journal =. 2017 , issn =. doi:https://doi.org/10.1016/j.tele.2017.04.013 , url =

    work page doi:10.1016/j.tele.2017.04.013 2017

  41. [41]

    Aggregation of preference relations to enhance the ranking quality of collaborative filtering based group recommender system , journal =

    Abinash Pujahari and Dilip Singh Sisodia , keywords =. Aggregation of preference relations to enhance the ranking quality of collaborative filtering based group recommender system , journal =. 2020 , issn =. doi:https://doi.org/10.1016/j.eswa.2020.113476 , url =

    work page doi:10.1016/j.eswa.2020.113476 2020

  42. [42]

    A summary of survey methodology best practices for security and privacy researchers , author=

  43. [43]

    Social psychological and personality science , volume=

    Misplaced confidences: Privacy and the control paradox , author=. Social psychological and personality science , volume=. 2013 , publisher=

    2013

  44. [44]

    Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing , pages=

    Privacy manipulation and acclimation in a location sharing application , author=. Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing , pages=

    2013

  45. [45]

    2017 European Intelligence and Security Informatics Conference (EISIC) , pages=

    Iot data profiles: The routines of your life reveals who you are , author=. 2017 European Intelligence and Security Informatics Conference (EISIC) , pages=. 2017 , organization=

    2017

  46. [46]

    Symposium on Usable Privacy and Security (SOUPS 2014) Workshop on Privacy Personas and Segmentation (PPS) , year=

    The privacy pragmatic as privacy vulnerable , author=. Symposium on Usable Privacy and Security (SOUPS 2014) Workshop on Privacy Personas and Segmentation (PPS) , year=

    2014

  47. [47]

    Wake Forest L

    Alan Westin's privacy homo economicus , author=. Wake Forest L. Rev. , volume=. 2014 , publisher=

    2014

  48. [48]

    2020 IEEE Symposium on Security and Privacy (SP) , pages=

    Ask the experts: What should be on an IoT privacy and security label? , author=. 2020 IEEE Symposium on Security and Privacy (SP) , pages=. 2020 , organization=

    2020

  49. [49]

    Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems , pages=

    Informing the design of a personalized privacy assistant for the internet of things , author=. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems , pages=

    2020

  50. [50]

    Isjlp , volume=

    The cost of reading privacy policies , author=. Isjlp , volume=. 2008 , publisher=

    2008

  51. [51]

    Berkeley Tech

    Disagreeable privacy policies: Mismatches between meaning and users' understanding , author=. Berkeley Tech. LJ , volume=. 2015 , publisher=

    2015

  52. [52]

    International Symposium on Privacy Enhancing Technologies Symposium , pages=

    A comparative study of online privacy policies and formats , author=. International Symposium on Privacy Enhancing Technologies Symposium , pages=. 2009 , organization=

    2009

  53. [53]

    Proceedings of the SIGCHI conference on Human Factors in Computing Systems , pages=

    Privacy policies as decision-making tools: an evaluation of online privacy notices , author=. Proceedings of the SIGCHI conference on Human Factors in Computing Systems , pages=

  54. [54]

    2016 IEEE 24th International Requirements Engineering Conference (RE) , pages=

    A theory of vagueness and privacy risk perception , author=. 2016 IEEE 24th International Requirements Engineering Conference (RE) , pages=. 2016 , organization=

    2016

  55. [55]

    Information, Communication & Society , volume=

    The biggest lie on the internet: Ignoring the privacy policies and terms of service policies of social networking services , author=. Information, Communication & Society , volume=. 2020 , publisher=

    2020

  56. [56]

    Journal of interactive marketing , volume=

    Strategies for reducing online privacy risks: Why consumers read (or don’t read) online privacy notices , author=. Journal of interactive marketing , volume=. 2004 , publisher=

    2004

  57. [57]

    A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , year =

    Ester, Martin and Kriegel, Hans-Peter and Sander, J\". A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , year =. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining , pages =

  58. [58]

    , biburl =

    Kaufman, Leonard and Rousseeuw, Peter J. , biburl =

  59. [59]

    Machine learning based privacy-preserving fair data trading in big data market , journal =

    Yanqi Zhao and Yong Yu and Yannan Li and Gang Han and Xiaojiang Du , keywords =. Machine learning based privacy-preserving fair data trading in big data market , journal =. 2019 , issn =. doi:https://doi.org/10.1016/j.ins.2018.11.028 , url =

    work page doi:10.1016/j.ins.2018.11.028 2019

  60. [60]

    How COVID-19 has pushed companies over the technology tipping point—and transformed business forever (https://mck.co/3trP4OV)

    McKinsey. How COVID-19 has pushed companies over the technology tipping point—and transformed business forever (https://mck.co/3trP4OV). 2020

    2020

  61. [61]

    OECD Digital Economy Outlook 2020

    OECD. OECD Digital Economy Outlook 2020. 2020. doi:https://doi.org/https://doi.org/10.1787/bb167041-en

    work page doi:10.1787/bb167041-en 2020

  62. [62]

    Can Machine Learning Help People Configure Their Mobile App Privacy Settings?

    Bin Liu. Can Machine Learning Help People Configure Their Mobile App Privacy Settings?. 2020. doi:10.1184/R1/11591340.v1

    work page doi:10.1184/r1/11591340.v1 2020

  63. [63]

    2019 , url =

    Marco Autili and Davide Di Ruscio and Paola Inverardi and Patrizio Pelliccione and Massimo Tivoli , title =. 2019 , url =

    2019

  64. [64]

    Proceedings of the 2010 IEEE International Conference on Data Mining , pages =

    Liu, Yanchi and Li, Zhongmou and Xiong, Hui and Gao, Xuedong and Wu, Junjie , title =. Proceedings of the 2010 IEEE International Conference on Data Mining , pages =. 2010 , isbn =. doi:10.1109/ICDM.2010.35 , abstract =

    work page doi:10.1109/icdm.2010.35 2010

  65. [65]

    Nature-Inspired Computation in Data Mining and Machine Learning , pages=

    Classification and clustering algorithms of machine learning with their applications , author=. Nature-Inspired Computation in Data Mining and Machine Learning , pages=. 2020 , publisher=

    2020

  66. [66]

    2021 , month=mar # " 23", publisher=

    Personalized privacy assistant , author=. 2021 , month=mar # " 23", publisher=

    2021

  67. [67]

    A simple and fast algorithm for K-medoids clustering , journal =

    Hae-Sang Park and Chi-Hyuck Jun , keywords =. A simple and fast algorithm for K-medoids clustering , journal =. 2009 , issn =. doi:https://doi.org/10.1016/j.eswa.2008.01.039 , url =

    work page doi:10.1016/j.eswa.2008.01.039 2009

  68. [68]

    and Georgiopoulos, M

    Bebis, G. and Georgiopoulos, M. , journal=. Feed-forward neural networks , year=

  69. [69]

    n.d.Bankruptcy Research Database

    Fawcett, Tom , title =. 2006 , issue_date =. doi:10.1016/j.patrec.2005.10.010 , journal =

    work page doi:10.1016/j.patrec.2005.10.010 2006

  70. [70]

    Zhu, Xingquan and Wu, Xindong , title =. Artif. Intell. Rev. , month = nov, pages =. 2004 , issue_date =

    2004

  71. [71]

    1993 , note =

    Feed-Forward Neural Networks in Chemistry: Mathematical Systems for Classification and Pattern Recognition , journal =. 1993 , note =

    1993

  72. [72]

    Moons, F., Vandervieren, E.,

    Minaee, Shervin and Kalchbrenner, Nal and Cambria, Erik and Nikzad, Narjes and Chenaghlu, Meysam and Gao, Jianfeng , title =. ACM Comput. Surv. , month = apr, articleno =. 2021 , issue_date =. doi:10.1145/3439726 , abstract =

    work page doi:10.1145/3439726 2021

  73. [73]

    P. T. 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS) , title=. 2019 , volume=. doi:10.1109/MODELS.2019.00011 , ISSN=

    work page doi:10.1109/models.2019.00011 2019

  74. [74]

    1997 , isbn =

    Gurney, Kevin , title =. 1997 , isbn =

    1997

  75. [75]

    2016 , isbn =

    Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron , title =. 2016 , isbn =

    2016

  76. [76]

    The software exoskeleton , author =

  77. [77]

    Westin , year =

    Alan F. Westin , year =. Bibliography of Surveys of the U.S. Public, 1970-2003 , url =

    1970

  78. [78]

    privacy pragmatists

    Most people are “privacy pragmatists” who, while concerned about privacy, will sometimes trade it off for other benefits , author=. The Harris Poll , volume=. 2003 , publisher=

    2003

  79. [79]

    Official Journal of the European Union , year=

    EU General Data Protection Regulation (GDPR) - Regulation EU 2016/679 of the European Parliament and of the Council of 27 April 2016 , author=. Official Journal of the European Union , year=

    2016

  80. [80]

    2012 IEEE 12th International Conference on Data Mining , pages=

    Mining permission request patterns from android and facebook applications , author=. 2012 IEEE 12th International Conference on Data Mining , pages=. 2012 , organization=

    2012

Showing first 80 references.