@inproceedings{chen-etal-2025-surveygen,
title = "{S}urvey{G}en-{I}: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing",
author = "Chen, Jing and
Yang, Zhiheng and
Shen, Yixian and
Liu, Jie and
Belloum, Adam and
Grosso, Paola and
Papagianni, Chrysa",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.193/",
pages = "3687--3714",
ISBN = "979-8-89176-298-5",
abstract = "Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce SurveyGen-I, an automatic survey generation framework that combines coarse-to-fine retrieval, adaptive planning, and memory-guided generation. SurveyGen-I performs survey-level retrieval to construct the initial outline and writing plan, then dynamically refines both during generation through a memory mechanism that stores previously written content and terminology, ensuring coherence across subsections. When the system detects insufficient context, it triggers fine-grained subsection-level retrieval. During generation, SurveyGen-I leverages this memory mechanism to maintain coherence across subsections. Experiments across six scientific domains demonstrate that SurveyGen-I consistently outperforms previous works in content quality, consistency, and citation coverage. The code is available at https://github.com/SurveyGens/SurveyGen-I."
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<abstract>Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce SurveyGen-I, an automatic survey generation framework that combines coarse-to-fine retrieval, adaptive planning, and memory-guided generation. SurveyGen-I performs survey-level retrieval to construct the initial outline and writing plan, then dynamically refines both during generation through a memory mechanism that stores previously written content and terminology, ensuring coherence across subsections. When the system detects insufficient context, it triggers fine-grained subsection-level retrieval. During generation, SurveyGen-I leverages this memory mechanism to maintain coherence across subsections. Experiments across six scientific domains demonstrate that SurveyGen-I consistently outperforms previous works in content quality, consistency, and citation coverage. The code is available at https://github.com/SurveyGens/SurveyGen-I.</abstract>
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%0 Conference Proceedings
%T SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
%A Chen, Jing
%A Yang, Zhiheng
%A Shen, Yixian
%A Liu, Jie
%A Belloum, Adam
%A Grosso, Paola
%A Papagianni, Chrysa
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F chen-etal-2025-surveygen
%X Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution by automating key steps in the survey-generation pipeline, such as retrieval, structuring, and summarization. However, existing LLM-based approaches often struggle with maintaining coherence across long, multi-section surveys and providing comprehensive citation coverage. To address these limitations, we introduce SurveyGen-I, an automatic survey generation framework that combines coarse-to-fine retrieval, adaptive planning, and memory-guided generation. SurveyGen-I performs survey-level retrieval to construct the initial outline and writing plan, then dynamically refines both during generation through a memory mechanism that stores previously written content and terminology, ensuring coherence across subsections. When the system detects insufficient context, it triggers fine-grained subsection-level retrieval. During generation, SurveyGen-I leverages this memory mechanism to maintain coherence across subsections. Experiments across six scientific domains demonstrate that SurveyGen-I consistently outperforms previous works in content quality, consistency, and citation coverage. The code is available at https://github.com/SurveyGens/SurveyGen-I.
%U https://aclanthology.org/2025.ijcnlp-long.193/
%P 3687-3714
Markdown (Informal)
[SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing](https://aclanthology.org/2025.ijcnlp-long.193/) (Chen et al., IJCNLP-AACL 2025)
ACL
- Jing Chen, Zhiheng Yang, Yixian Shen, Jie Liu, Adam Belloum, Paola Grosso, and Chrysa Papagianni. 2025. SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 3687–3714, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.