arXiv 2604.275502026-04-30cs.CLcs.AI

APPSI-139: 一个平行语料库，用于英语应用隐私政策的摘要与解释

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

Tianjin University（天津大学）；Zhejiang University（浙江大学）；North University of China（北方工业大学）；Institute of Science Tokyo（东京科学研究所）；The Hong Kong Polytechnic University（香港理工大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）

Pengyun Zhu, Qiheng Sun, Long Wen, Yanbo Wang, Yang Cao, Junxu Liu, Deyi Xiong, Jinfei Liu, Zhibo Wang, Kui Ren

备注：: Accepted to ACL 2026 Main Conference

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AI总结：

本文提出APPSI-139平行语料库，包含139个隐私政策及15,692个改写语料，结合TCSI-pp-V2框架提升摘要与解释的可读性与可靠性，优于GPT-4o等大模型。

摘要：

隐私政策是用户理解服务提供商如何处理个人数据的关键文件。然而，这些文档通常冗长复杂，充满技术术语和法律术语，导致用户无意中接受可能与法律冲突的条款。尽管摘要和解释这些隐私政策至关重要，但缺乏高质量的英语平行语料库，以优化法律清晰度和可读性。为解决此问题，我们引入了APPSI-139，一个由领域专家精心标注的高质量英语隐私政策语料库，专门用于摘要和解释任务。该语料库包含139个英语隐私政策、15,692个改写平行语料和36,351个细粒度注释标签，覆盖11种数据实践类别。同时，我们提出了TCSI-pp-V2框架，采用交替训练策略，协调多个专家模块，有效平衡计算效率和准确性。实验结果表明，基于APPSI-139语料库和TCSI-pp-V2框架构建的混合摘要系统在可读性和可靠性方面优于GPT-4o等大语言模型。源代码和数据集可在https://github.com/EnlightenedAI/APPSI-139获取。

英文摘要：

Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and complex, as well as filled with technobabble and legalese, causing users to unknowingly accept terms that may even contradict the law. While summarizing and interpreting these privacy policies is crucial, there is a lack of high-quality English parallel corpus optimized for legal clarity and readability. To address this issue, we introduce APPSI-139, a high-quality English privacy policy corpus meticulously annotated by domain experts, specifically designed for summarization and interpretation tasks. The corpus includes 139 English privacy policies, 15,692 rewritten parallel corpora, and 36,351 fine-grained annotation labels across 11 data practice categories. Concurrently, we propose TCSI-pp-V2, a hybrid privacy policy summarization and interpretation framework that employs an alternating training strategy and coordinates multiple expert modules to effectively balance computational efficiency and accuracy. Experimental results show that the hybrid summarization system built on APPSI-139 corpus and the TCSI-pp-V2 framework outperform large language models, such as GPT-4o and LLaMA-3-70B, in terms of readability and reliability. The source code and dataset are available at https://github.com/EnlightenedAI/APPSI-139.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。