Plans for Evaluating Structured Generative Search Summaries
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866913162494738432 |
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| author | Sakai, Tetsuya Lee, Jina Fang, Hanpei Song, Young-In |
| author_facet | Sakai, Tetsuya Lee, Jina Fang, Hanpei Song, Young-In |
| contents | We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, generated by a large language model, typically consists of an overview, several sections with section titles, and a list of source documents that are cited within the summary. We then describe our plans for implementing and evaluating the framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26400 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Plans for Evaluating Structured Generative Search Summaries Sakai, Tetsuya Lee, Jina Fang, Hanpei Song, Young-In Information Retrieval Artificial Intelligence We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, generated by a large language model, typically consists of an overview, several sections with section titles, and a list of source documents that are cited within the summary. We then describe our plans for implementing and evaluating the framework. |
| title | Plans for Evaluating Structured Generative Search Summaries |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2605.26400 |