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home / papers / 2026 / APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation

APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation

Paper · 2026 · Pengyun Zhu, Qiheng Sun, Long Wen et al.

Summary

App privacy policies are long and full of legal and technical jargon, so users often accept terms they do not understand. Earlier privacy policy corpora mostly support classification or information extraction. The English version of the prior CAPP-130 was machine-translated, so no expert-built English resource existed for making policies both shorter and clearer. The authors release APPSI-139, built from the policies of 139 popular apps in areas such as shopping, streaming, sports and gaming. A legal team annotated it with data-practice categories and special tags, and rewrote difficult clauses in plain language. On this data they build TCSI-pp-V2, a hybrid extractive-abstractive summarizer based on multi-task learning. It uses five parameter-sharing experts (Importance, Risk, Sensitivity, Topic, Rewrite) trained alternately over a shared encoder. The system outperforms prompted LLMs on classification and rewriting metrics, and questionnaire studies rate its summaries above GPT-4o for readability and reliability. It also needs far fewer resources than the earlier TCSI-pp. The work provides an expert-grade benchmark for simplifying legal text and building user-facing privacy tools.

Key points

  • APPSI-139 contents: 139 English policies (about 30.9k sentences), 15,692 expert-rewritten parallel pairs, and 36,351 labels covering 11 data-practice categories plus 3 special tags (core clause, sensitive information, risk).
  • Annotation team: the guidelines were drafted over several pilot rounds by a law professor and five lawyers, building on the CAPP-130 design.
  • Efficiency gains over TCSI-pp (both on an mT5-small backbone):
    • encoding time drops by roughly 73%;
    • per-sentence processing time is nearly halved;
    • peak GPU memory falls from 7,343 MB to 2,766 MB.
  • Comparison with LLMs on APPSI-139: TCSI-pp-V2 beats Qwen3-8B, Llama3-8B, GPT-4o-mini and Gemini-2.5 on most tasks.
    • Topic Micro F1: 78.18
    • Sensitive Micro F1: 96.96
    • Rewriting: ROUGE-L 0.6903, BERTScore 0.9430
  • Robustness and limits: performance stays stable on both the longest and shortest inputs. The work currently covers only English policies, and code and data are available on GitHub (EnlightenedAI/APPSI-139).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🎓Hong Kong Polytechnic UniversityHong Kong · China84 works · 4,956 citations🎓Tianjin UniversityTianjin · China41 works · 1,128 citations🎓Institute of Science TokyoTokyo · Japan10 works · 20 citations

Also on the byline: North University of China.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 works

🧭 Topics 3Research subjects and languages the work is about.English · 2,263 worksSummarization · 232 worksLegal AI · 134 works

Cite

Cite as Pengyun Zhu, Qiheng Sun, Long Wen, Yanbo Wang, Yang Cao, Junxu Liu, Deyi Xiong, Jinfei Liu, Zhibo Wang and Kui Ren. 2026. APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.168

@inproceedings{zhu2026appsi,
  title = {{APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation}},
  author = {Pengyun Zhu, Qiheng Sun, Long Wen, Yanbo Wang, Yang Cao, Junxu Liu, Deyi Xiong, Jinfei Liu, Zhibo Wang and Kui Ren},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.168},
  url = {https://doi.org/10.18653/v1/2026.acl-long.168}
}

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