RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929685438398464 |
|---|---|
| author | Santosh, T. Y. S. S. Jia, Chen Goroncy, Patrick Grabmair, Matthias |
| author_facet | Santosh, T. Y. S. S. Jia, Chen Goroncy, Patrick Grabmair, Matthias |
| contents | This paper addresses the task of legal summarization, which involves distilling complex legal documents into concise, coherent summaries. Current approaches often struggle with content theme deviation and inconsistent writing styles due to their reliance solely on source documents. We propose RELexED, a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. RELexED employs a two-stage exemplar selection strategy, leveraging a determinantal point process to balance the trade-off between similarity of exemplars to the query and diversity among exemplars, with scores computed via influence functions. Experimental results on two legal summarization datasets demonstrate that RELexED significantly outperforms models that do not utilize exemplars and those that rely solely on similarity-based exemplar selection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity Santosh, T. Y. S. S. Jia, Chen Goroncy, Patrick Grabmair, Matthias Computation and Language This paper addresses the task of legal summarization, which involves distilling complex legal documents into concise, coherent summaries. Current approaches often struggle with content theme deviation and inconsistent writing styles due to their reliance solely on source documents. We propose RELexED, a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. RELexED employs a two-stage exemplar selection strategy, leveraging a determinantal point process to balance the trade-off between similarity of exemplars to the query and diversity among exemplars, with scores computed via influence functions. Experimental results on two legal summarization datasets demonstrate that RELexED significantly outperforms models that do not utilize exemplars and those that rely solely on similarity-based exemplar selection. |
| title | RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.14113 |