SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems

• URL: http://arxiv.org/abs/2508.11310v1
• Date: Fri, 15 Aug 2025 08:27:58 GMT
• Title: SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems
• Authors: Beichen Guo, Zhiyuan Wen, Yu Yang, Peng Gao, Ruosong Yang, Jiaxing Shen,
• Abstract summary: SGSimEval is a comprehensive benchmark for Survey Generation with Similarity-Enhanced Evaluation.<n>We introduce human preference metrics that emphasize both inherent quality and similarity to humans.<n>Our experiments reveal that current ASG systems demonstrate human-comparable superiority in outline generation.
• Score: 26.888698710786507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing interest in automatic survey generation (ASG), a task that traditionally required considerable time and effort, has been spurred by recent advances in large language models (LLMs). With advancements in retrieval-augmented generation (RAG) and the rising popularity of multi-agent systems (MASs), synthesizing academic surveys using LLMs has become a viable approach, thereby elevating the need for robust evaluation methods in this domain. However, existing evaluation methods suffer from several limitations, including biased metrics, a lack of human preference, and an over-reliance on LLMs-as-judges. To address these challenges, we propose SGSimEval, a comprehensive benchmark for Survey Generation with Similarity-Enhanced Evaluation that evaluates automatic survey generation systems by integrating assessments of the outline, content, and references, and also combines LLM-based scoring with quantitative metrics to provide a multifaceted evaluation framework. In SGSimEval, we also introduce human preference metrics that emphasize both inherent quality and similarity to humans. Extensive experiments reveal that current ASG systems demonstrate human-comparable superiority in outline generation, while showing significant room for improvement in content and reference generation, and our evaluation metrics maintain strong consistency with human assessments.

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