A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
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.
