AspirinSum：一种基于方面的效用保留去标识化摘要框架
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework
AI总结：
针对文本数据高效通用去标识化方法匮乏、难适配特定领域的问题，本研究提出AspirinSum框架，通过对齐专家方面维度实现敏感文档的效用保留去标识化摘要，可用于数据发布并支撑下游任务。
摘要：
随着大语言模型（Large Language Model, LLM）的快速发展，整个研究领域都在积极获取各类可用文本数据以训练LLM。目前，大部分可用文本数据均从互联网收集，被视为低成本的训练数据来源。然而，当研究者试图将LLM的能力拓展至医疗、教育等个人相关领域时，这些领域公开数据集的匮乏导致LLM在这类场景的适配进展缓慢，而数据集难以公开的核心原因是其通常包含个人敏感信息。为符合隐私法规要求，这类领域的数据在任何形式的传播前都必须经过去标识化处理。现有研究已针对图像或表格数据的去标识化问题开展了大量工作，但针对文本数据的高效通用去标识化方法研究仍较为有限，多数方法依赖人工标注或预定义类别列表，难以快速适配特定领域。本研究的目标是开发一款可轻松适配特定领域、能利用现有专家知识且无需额外人工标注的文本去标识化框架。我们提出了基于方面的效用保留去标识化摘要框架AspirinSum，通过从现有评注数据中学习对齐专家的方面维度，该框架能够提取个人敏感方面对应的子句，并用相似方面的子句进行替换，从而高效完成个人敏感文档的摘要与去标识化。我们预期经去标识化处理的文本可应用于数据发布场景，最终将我们构建的去标识化数据集公开，供下游任务使用。
英文摘要：
Due to the rapid advancement of Large Language Model (LLM), the whole community eagerly consumes any available text data in order to train the LLM. Currently, large portion of the available text data are collected from internet, which has been thought as a cheap source of the training data. However, when people try to extend the LLM's capability to the personal related domain, such as healthcare or education, the lack of public dataset in these domains make the adaption of the LLM in such domains much slower. The reason of lacking public available dataset in such domains is because they usually contain personal sensitive information. In order to comply with privacy law, the data in such domains need to be de-identified before any kind of dissemination. It had been much research tried to address this problem for the image or tabular data. However, there was limited research on the efficient and general de-identification method for text data. Most of the method based on human annotation or predefined category list. It usually can not be easily adapted to specific domains. The goal of this proposal is to develop a text de-identification framework, which can be easily adapted to the specific domain, leverage the existing expert knowledge without further human annotation. We propose an aspect-based utility-preserved de-identification summarization framework, AspirinSum, by learning to align expert's aspect from existing comment data, it can efficiently summarize the personal sensitive document by extracting personal sensitive aspect related sub-sentence and de-identify it by substituting it with similar aspect sub-sentence. We envision that the de-identified text can then be used in data publishing, eventually publishing our de-identified dataset for downstream task use.
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。
