A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data

TL;DR

This paper introduces Summation, a personalized, reinforcement learning-based hierarchical summarization system that adapts to user preferences to generate concise, structured summaries from large document collections, improving information extraction.

Contribution

It presents a novel hierarchical concept-based summarization framework that learns user preferences through reinforcement learning for personalized document summaries.

Findings

Effective personalization of summaries based on user preferences

Improved comprehension and navigation of large document collections

Adaptive summaries outperform generic approaches

Abstract

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.