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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity
%A T.y.s.s, Santosh
%A Jia, Chen
%A Goroncy, Patrick
%A Grabmair, Matthias
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F t-y-s-s-etal-2025-relexed
%X 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.
%R 10.18653/v1/2025.findings-naacl.26
%U https://aclanthology.org/2025.findings-naacl.26/
%U https://doi.org/10.18653/v1/2025.findings-naacl.26
%P 427-434

Markdown (Informal)

[RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity](https://aclanthology.org/2025.findings-naacl.26/) (T.y.s.s et al., Findings 2025)

• RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (T.y.s.s et al., Findings 2025)

ACL

• Santosh T.y.s.s, Chen Jia, Patrick Goroncy, and Matthias Grabmair. 2025. RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 427–434, Albuquerque, New Mexico. Association for Computational Linguistics.