@inproceedings{luo-etal-2025-dtcrs,
title = "{DTCRS}: Dynamic Tree Construction for Recursive Summarization",
author = "Luo, Guanran and
Jian, Zhongquan and
Qiu, Wentao and
Wang, Meihong and
Wu, Qingqiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.536/",
doi = "10.18653/v1/2025.acl-long.536",
pages = "10948--10963",
ISBN = "979-8-89176-251-0",
abstract = "Retrieval-Augmented Generation (RAG) mitigates the hallucination problem of Large Language Models (LLMs) by incorporating external knowledge. Recursive summarization constructs a hierarchical summary tree by clustering text chunks, integrating information from multiple parts of a document to provide evidence for abstractive questions involving multi-step reasoning. However, summary trees often contain a large number of redundant summary nodes, which not only increase construction time but may also negatively impact question answering. Moreover, recursive summarization is not suitable for all types of questions. We introduce DTCRS, a method that dynamically generates summary trees based on document structure and query semantics. DTCRS determines whether a summary tree is necessary by analyzing the question type. It then decomposes the question and uses the embeddings of sub-questions as initial cluster centers, reducing redundant summaries while improving the relevance between summaries and the question. Our approach significantly reduces summary tree construction time and achieves substantial improvements across three QA tasks. Additionally, we investigate the applicability of recursive summarization to different question types, providing valuable insights for future research."
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%0 Conference Proceedings
%T DTCRS: Dynamic Tree Construction for Recursive Summarization
%A Luo, Guanran
%A Jian, Zhongquan
%A Qiu, Wentao
%A Wang, Meihong
%A Wu, Qingqiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F luo-etal-2025-dtcrs
%X Retrieval-Augmented Generation (RAG) mitigates the hallucination problem of Large Language Models (LLMs) by incorporating external knowledge. Recursive summarization constructs a hierarchical summary tree by clustering text chunks, integrating information from multiple parts of a document to provide evidence for abstractive questions involving multi-step reasoning. However, summary trees often contain a large number of redundant summary nodes, which not only increase construction time but may also negatively impact question answering. Moreover, recursive summarization is not suitable for all types of questions. We introduce DTCRS, a method that dynamically generates summary trees based on document structure and query semantics. DTCRS determines whether a summary tree is necessary by analyzing the question type. It then decomposes the question and uses the embeddings of sub-questions as initial cluster centers, reducing redundant summaries while improving the relevance between summaries and the question. Our approach significantly reduces summary tree construction time and achieves substantial improvements across three QA tasks. Additionally, we investigate the applicability of recursive summarization to different question types, providing valuable insights for future research.
%R 10.18653/v1/2025.acl-long.536
%U https://aclanthology.org/2025.acl-long.536/
%U https://doi.org/10.18653/v1/2025.acl-long.536
%P 10948-10963
Markdown (Informal)
[DTCRS: Dynamic Tree Construction for Recursive Summarization](https://aclanthology.org/2025.acl-long.536/) (Luo et al., ACL 2025)
ACL
- Guanran Luo, Zhongquan Jian, Wentao Qiu, Meihong Wang, and Qingqiang Wu. 2025. DTCRS: Dynamic Tree Construction for Recursive Summarization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10948–10963, Vienna, Austria. Association for Computational Linguistics.