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.

该论文提出了一种基于个性化概念的分层式摘要方法 Summation，使用强化学习算法生成特定主题的未见文档的个性化摘要，以增强理解、有效导航和满足用户独特需求从大量文献中提取有意义的见解。