# A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization

arXiv: https://arxiv.org/abs/2503.06888 · HTML: https://mlsanity.com/paper/2503.06888/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 10 Mar 2025.

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

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Object Detection for Medical Image Analysis: Insights from the RT-DETR Model](https://mlsanity.com/paper/2501.16469/) (2025)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large Data Sets](https://mlsanity.com/paper/2412.19420/) (2024)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [A Deep Learning Approach to Interface Color Quality Assessment in HCI](https://mlsanity.com/paper/2502.09914/) (2025)
- [Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries](https://mlsanity.com/paper/2403.01002/) (2024)
- [Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data](https://mlsanity.com/paper/2412.20345/) (2024)

## Built on by (most cited first, 5 of 5)

- [Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction](https://mlsanity.com/paper/2507.01437/) (2025)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction](https://mlsanity.com/paper/2507.00540/) (2025)
- [A Deep Learning Framework for Boundary-Aware Semantic Segmentation](https://mlsanity.com/paper/2503.22050/) (2025)
- [Improving Harmful Text Detection with Joint Retrieval and External Knowledge](https://mlsanity.com/paper/2504.02310/) (2025)

## Abstract (arXiv)

This paper proposes a medical text summarization method based on LongFormer, aimed at addressing the challenges faced by existing models when processing long medical texts. Traditional summarization methods are often limited by short-term memory, leading to information loss or reduced summary quality in long texts. LongFormer, by introducing long-range self-attention, effectively captures long-range dependencies in the text, retaining more key information and improving the accuracy and information retention of summaries. Experimental results show that the LongFormer-based model outperforms traditional models, such as RNN, T5, and BERT in automatic evaluation metrics like ROUGE. It also receives high scores in expert evaluations, particularly excelling in information retention and grammatical accuracy. However, there is still room for improvement in terms of conciseness and readability. Some experts noted that the generated summaries contain redundant information, which affects conciseness. Future research will focus on further optimizing the model structure to enhance conciseness and fluency, achieving more efficient medical text summarization. As medical data continues to grow, automated summarization technology will play an increasingly important role in fields such as medical research, clinical decision support, and knowledge management.