# Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction

arXiv: https://arxiv.org/abs/2502.10388 · HTML: https://mlsanity.com/paper/2502.10388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Universal Self-Consistency for Large Language Model Generation](https://mlsanity.com/paper/2311.17311/) (2023)
- [On Learning to Summarize with Large Language Models as References](https://mlsanity.com/paper/2305.14239/) (2023)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [A Comparative Study of Pretrained Language Models for Long Clinical Text](https://mlsanity.com/paper/2301.11847/) (2023)
- [Closing the gap between open-source and commercial large language models for medical evidence summarization](https://mlsanity.com/paper/2408.00588/) (2024)
- [ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications](https://mlsanity.com/paper/2403.05303/) (2024)
- [MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens](https://mlsanity.com/paper/2406.15019/) (2024)
- [Dynamic Prompting: A Unified Framework for Prompt Tuning](https://mlsanity.com/paper/2303.02909/) (2023)
- [Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens](https://mlsanity.com/paper/2305.04241/) (2023)
- [Classifying Long Clinical Documents with Pre-trained Transformers](https://mlsanity.com/paper/2105.06752/) (2021)
- [Analysis of Risk Factor Domains in Psychosis Patient Health Records](https://mlsanity.com/paper/1809.05752/) (2018)
- [Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data](https://mlsanity.com/paper/2403.19511/) (2024)

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

- [RASPER: Reward-Aligned Summarization of Clinical Notes for EHR Outcome Prediction](https://mlsanity.com/paper/2610.02979/) (2026)

## Abstract (arXiv)

Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal. One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary. However, the summarization process inevitably results in some loss of information. In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document. We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different information signals, and we propose methods to measure these differences. We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models. We validate our hypotheses on a high-impact task -- 30-day readmission prediction from a psychiatric discharge -- using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.