CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines

Fuente: arXiv

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Bibliographic Details
Main Authors:	Chen, Qiyuan, Chen, Jiahe, Huang, Hongsen, Shao, Qian, Chen, Jintai, Hua, Renjie, Xu, Hongxia, Wu, Ruijia, Chuan, Ren, Wu, Jian
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
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author	Chen, Qiyuan Chen, Jiahe Huang, Hongsen Shao, Qian Chen, Jintai Hua, Renjie Xu, Hongxia Wu, Ruijia Chuan, Ren Wu, Jian
author_facet	Chen, Qiyuan Chen, Jiahe Huang, Hongsen Shao, Qian Chen, Jintai Hua, Renjie Xu, Hongxia Wu, Ruijia Chuan, Ren Wu, Jian
contents	Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift raises a central question for content creators: How can we reliably quantify a source article's influence on a GSE's synthesized answer across diverse intents and follow-up questions? We introduce CC-GSEO-Bench, a content-centric benchmark that couples a large-scale dataset with a creator-centered evaluation framework. The dataset contains over 1,000 source articles and over 5,000 query-article pairs, organized in a one-to-many structure for article-level evaluation. We ground construction in realistic retrieval by combining seed queries from public QA datasets with limited synthesized augmentation and retaining only queries whose paired source reappears in a follow-up retrieval step. On top of this dataset, we operationalize influence along three core dimensions: Exposure, Faithful Credit, and Causal Impact, and two content-quality dimensions: Readability and Structure, and Trustworthiness and Safety. We aggregate query-level signals over each article's query cluster to summarize influence strength, coverage, and stability, and empirically characterize influence dynamics across representative content patterns.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_05607
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines Chen, Qiyuan Chen, Jiahe Huang, Hongsen Shao, Qian Chen, Jintai Hua, Renjie Xu, Hongxia Wu, Ruijia Chuan, Ren Wu, Jian Computation and Language Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift raises a central question for content creators: How can we reliably quantify a source article's influence on a GSE's synthesized answer across diverse intents and follow-up questions? We introduce CC-GSEO-Bench, a content-centric benchmark that couples a large-scale dataset with a creator-centered evaluation framework. The dataset contains over 1,000 source articles and over 5,000 query-article pairs, organized in a one-to-many structure for article-level evaluation. We ground construction in realistic retrieval by combining seed queries from public QA datasets with limited synthesized augmentation and retaining only queries whose paired source reappears in a follow-up retrieval step. On top of this dataset, we operationalize influence along three core dimensions: Exposure, Faithful Credit, and Causal Impact, and two content-quality dimensions: Readability and Structure, and Trustworthiness and Safety. We aggregate query-level signals over each article's query cluster to summarize influence strength, coverage, and stability, and empirically characterize influence dynamics across representative content patterns.
title	CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines
topic	Computation and Language
url	https://arxiv.org/abs/2509.05607