@inproceedings{shukla-etal-2025-graphrag,
title = "{G}raph{RAG} Analysis for Financial Narrative Summarization and A Framework for Optimizing Domain Adaptation",
author = "Shukla, Neelesh Kumar and
Prabhakar, Prabhat and
Thangaraj, Sakthivel and
Singh, Sandeep and
Sun, Weiyi and
Venkatesan, C Prasanna and
Krishnamurthy, Viji",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.2/",
pages = "23--34",
abstract = "Large Language Models (LLMs) have shown promise in summarizing complex documents, but their limitations in handling lengthy documents and capturing global information hinder their performance in tasks like Query-Focused Summarization (QFS). We explore GraphRAG, a retrieval-augmented generation approach that utilizes a globally summarized knowledge graph derived from an LLM. We apply GraphRAG to the Financial Narrative Summarization (FNS) dataset, which consists of lengthy financial reports. Our results show that a naive RAG approach outperforms GraphRAG in terms of comprehensiveness, directness, conciseness and completeness. However, we demonstrate that optimizing entity and relation extraction using an LLM as an optimizer can enhance GraphRAG`s performance. Our study highlights the need for domain-specific optimization to improve GraphRAG`s capabilities for summarization tasks in facts-heavy domains like finance. We propose an optimization framework that extends GraphRAG`s original domain adaptation strategy by incorporating entity and relations optimization, leading to improved performance in capturing relevant entities and relationships. Our findings contribute to the development of more effective summarization models for complex documents in finance and other domains."
}
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%0 Conference Proceedings
%T GraphRAG Analysis for Financial Narrative Summarization and A Framework for Optimizing Domain Adaptation
%A Shukla, Neelesh Kumar
%A Prabhakar, Prabhat
%A Thangaraj, Sakthivel
%A Singh, Sandeep
%A Sun, Weiyi
%A Venkatesan, C. Prasanna
%A Krishnamurthy, Viji
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F shukla-etal-2025-graphrag
%X Large Language Models (LLMs) have shown promise in summarizing complex documents, but their limitations in handling lengthy documents and capturing global information hinder their performance in tasks like Query-Focused Summarization (QFS). We explore GraphRAG, a retrieval-augmented generation approach that utilizes a globally summarized knowledge graph derived from an LLM. We apply GraphRAG to the Financial Narrative Summarization (FNS) dataset, which consists of lengthy financial reports. Our results show that a naive RAG approach outperforms GraphRAG in terms of comprehensiveness, directness, conciseness and completeness. However, we demonstrate that optimizing entity and relation extraction using an LLM as an optimizer can enhance GraphRAG‘s performance. Our study highlights the need for domain-specific optimization to improve GraphRAG‘s capabilities for summarization tasks in facts-heavy domains like finance. We propose an optimization framework that extends GraphRAG‘s original domain adaptation strategy by incorporating entity and relations optimization, leading to improved performance in capturing relevant entities and relationships. Our findings contribute to the development of more effective summarization models for complex documents in finance and other domains.
%U https://aclanthology.org/2025.finnlp-1.2/
%P 23-34
Markdown (Informal)
[GraphRAG Analysis for Financial Narrative Summarization and A Framework for Optimizing Domain Adaptation](https://aclanthology.org/2025.finnlp-1.2/) (Shukla et al., FinNLP 2025)
ACL
- Neelesh Kumar Shukla, Prabhat Prabhakar, Sakthivel Thangaraj, Sandeep Singh, Weiyi Sun, C Prasanna Venkatesan, and Viji Krishnamurthy. 2025. GraphRAG Analysis for Financial Narrative Summarization and A Framework for Optimizing Domain Adaptation. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 23–34, Abu Dhabi, UAE. Association for Computational Linguistics.