---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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{}
---

<!-- # Model Card for Model ID -->

<!-- Provide a quick summary of what the model is/does. -->

<!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). -->

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

Longformer-Encoder-Decoder (LED) model is designed to address the challenge of summarizing lengthy English texts, particularly legal documents. Utilizing a local-global attention mechanism, LED is capable of handling longer input sequences efficiently, making it highly suitable for legal document summarization tasks.

## Citation

**BibTeX:**
```
@misc{duc2023led,
  author = {Chu Đình Đức},
  title = {Longformer-Encoder-Decoder for Legal Document Summarization},
  year = {2023},
}
```