# SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback

arXiv: https://arxiv.org/abs/2408.07294 · HTML: https://mlsanity.com/paper/2408.07294/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 73 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [Multi-Reward Reinforced Summarization with Saliency and Entailment](https://mlsanity.com/paper/1804.06451/) (2018)
- [Can Neural Machine Translation be Improved with User Feedback?](https://mlsanity.com/paper/1804.05958/) (2018)
- [The price of debiasing automatic metrics in natural language evaluation](https://mlsanity.com/paper/1807.02202/) (2018)
- [Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning](https://mlsanity.com/paper/1805.10627/) (2018)
- [Improving Abstraction in Text Summarization](https://mlsanity.com/paper/1808.07913/) (2018)
- [Bandit Structured Prediction for Neural Sequence-to-Sequence Learning](https://mlsanity.com/paper/1704.06497/) (2017)
- [Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation](https://mlsanity.com/paper/1907.12894/) (2019)
- [APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning](https://mlsanity.com/paper/1808.09658/) (2018)
- [Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing](https://mlsanity.com/paper/1811.12239/) (2018)
- [Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming](https://mlsanity.com/paper/2004.07822/) (2020)

## Built on by (most cited first, 4 of 4)

- [SlideTailor: Personalized Presentation Slide Generation for Scientific Papers](https://mlsanity.com/paper/2512.20292/) (2025)
- [POPI: Personalizing LLMs via Optimized Natural Language Preference Inference](https://mlsanity.com/paper/2510.17881/) (2025)
- [Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization](https://mlsanity.com/paper/2409.07335/) (2024)
- [H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages](https://mlsanity.com/paper/2508.05628/) (2025)

## Abstract (arXiv)

Existing multi-document summarization approaches produce a uniform summary for all users without considering individuals' interests, which is highly impractical. Making a user-specific summary is a challenging task as it requires: i) acquiring relevant information about a user; ii) aggregating and integrating the information into a user-model; and iii) utilizing the provided information in making the personalized summary. Therefore, in this paper, we propose a solution to a substantial and challenging problem in summarization, i.e., recommending a summary for a specific user. The proposed approach, called SumRecom, brings the human into the loop and focuses on three aspects: personalization, interaction, and learning user's interest without the need for reference summaries. SumRecom has two steps: i) The user preference extractor to capture users' inclination in choosing essential concepts, and ii) The summarizer to discover the user's best-fitted summary based on the given feedback. Various automatic and human evaluations on the benchmark dataset demonstrate the supremacy SumRecom in generating user-specific summaries. Document summarization and Interactive summarization and Personalized summarization and Reinforcement learning.