The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights. Traditional document summarization approaches often fail to meet individual user requirements and lack structure for efficient information processing. To address these limitations, we propose Summation, a hierarchical personalized concept-based summarization approach. It synthesizes documents into a concise hierarchical concept map and actively engages users by learning and adapting to their preferences. Using a Reinforcement Learning algorithm, Summation generates personalized summaries for unseen documents on specific topics. This framework enhances comprehension, enables effective navigation, and empowers users to extract meaningful insights from large document collections aligned with their unique requirements.

翻译：文本数据的指数级增长催生了迫切需要能够帮助用户提取有意义信息的工具。传统的文档摘要方法往往无法满足个体用户的需求，且缺乏高效信息处理所需的结构。为解决这些局限，我们提出Summation——一种层次化个性化概念摘要方法。它将文档综合为简洁的层次化概念图，并通过学习并适应用户偏好主动与之交互。借助强化学习算法，Summation能为特定主题的未见文档生成个性化摘要。该框架增强了理解能力，实现了高效导航，并帮助用户从大量文档集合中提取符合其独特需求的有意义信息。