A Personalized Reinforcement Learning Summarization Service for Learning
  Structure from Unstructured Data
Samira Ghodratnama Amin Beheshti Mehrdad Zakershahrak
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.