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

arXiv: https://arxiv.org/abs/2307.05696 · HTML: https://mlsanity.com/paper/2307.05696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Can Neural Machine Translation be Improved with User Feedback?](https://mlsanity.com/paper/1804.05958/) (2018)
- [Learning to Extract Coherent Summary via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.07036/) (2018)
- [A Click Sequence Model for Web Search](https://mlsanity.com/paper/1805.03411/) (2018)
- [Towards Personalized and Human-in-the-Loop Document Summarization](https://mlsanity.com/paper/2108.09443/) (2021)
- [Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing](https://mlsanity.com/paper/1811.12239/) (2018)
- [Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning](https://mlsanity.com/paper/2012.11792/) (2020)
- [Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies](https://mlsanity.com/paper/2012.15755/) (2020)

## Built on by (most cited first, 1 of 1)

- [Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications](https://mlsanity.com/paper/2311.12287/) (2023)

## Abstract (arXiv)

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.