A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
It addresses the need for personalized tools to extract insights from unstructured textual data for users dealing with large document collections.
The paper tackles the problem of traditional document summarization failing to meet individual user needs by proposing Summation, a hierarchical personalized concept-based approach that uses Reinforcement Learning to generate tailored summaries, enhancing comprehension and navigation for users.
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.