Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916023601463296 |
|---|---|
| author | Huang, Ying-Hsiang Gong, Claire Shaji, Shreya Yan, Alison Harka, Leslie Du, Albert Gopal, Anjali Klein, Samuel J Shen, Shannon Zejiang Phillips, Mark Owens, Trevor Deeds, Kyle Lee, Benjamin Charles Germain |
| author_facet | Huang, Ying-Hsiang Gong, Claire Shaji, Shreya Yan, Alison Harka, Leslie Du, Albert Gopal, Anjali Klein, Samuel J Shen, Shannon Zejiang Phillips, Mark Owens, Trevor Deeds, Kyle Lee, Benjamin Charles Germain |
| contents | Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data. The End of Term Web Archive alone contains, among other file types, millions of PDFs produced by the federal government. While preservation with web archives has been successful, significant challenges for access and discoverability remain. For example, current affordances for browsing the End of Term PDFs are limited to downloading and browsing individual PDFs, as well as performing basic keyword search across them. In this paper, we introduce GovScape, a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) - to our knowledge, all renderable PDFs in the 2020 crawl that are 50 pages or under. GovScape supports four primary forms of search over these 10 million PDFs: in addition to providing (1) filter conditions over metadata facets including domain and crawl date and (2) exact text search against the PDF text, we provide (3) semantic text search and (4) visual search against the PDFs across individual pages, enabling users to structure queries such as "redacted documents" or "pie charts." We detail the constituent components of GovScape, including the search affordances, embedding pipeline, system architecture, and open source codebase. Significantly, the total estimated compute cost for GovScape's pre-processing pipeline for 10 million PDFs was approximately $1,500, equivalent to 47,000 PDF pages per dollar spent on compute, demonstrating the potential for immediate scalability. Accordingly, we outline steps that we have already begun pursuing toward multimodal search at the 100+ million PDF scale. GovScape can be found at https://www.govscape.net. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs Huang, Ying-Hsiang Gong, Claire Shaji, Shreya Yan, Alison Harka, Leslie Du, Albert Gopal, Anjali Klein, Samuel J Shen, Shannon Zejiang Phillips, Mark Owens, Trevor Deeds, Kyle Lee, Benjamin Charles Germain Information Retrieval Digital Libraries Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data. The End of Term Web Archive alone contains, among other file types, millions of PDFs produced by the federal government. While preservation with web archives has been successful, significant challenges for access and discoverability remain. For example, current affordances for browsing the End of Term PDFs are limited to downloading and browsing individual PDFs, as well as performing basic keyword search across them. In this paper, we introduce GovScape, a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) - to our knowledge, all renderable PDFs in the 2020 crawl that are 50 pages or under. GovScape supports four primary forms of search over these 10 million PDFs: in addition to providing (1) filter conditions over metadata facets including domain and crawl date and (2) exact text search against the PDF text, we provide (3) semantic text search and (4) visual search against the PDFs across individual pages, enabling users to structure queries such as "redacted documents" or "pie charts." We detail the constituent components of GovScape, including the search affordances, embedding pipeline, system architecture, and open source codebase. Significantly, the total estimated compute cost for GovScape's pre-processing pipeline for 10 million PDFs was approximately $1,500, equivalent to 47,000 PDF pages per dollar spent on compute, demonstrating the potential for immediate scalability. Accordingly, we outline steps that we have already begun pursuing toward multimodal search at the 100+ million PDF scale. GovScape can be found at https://www.govscape.net. |
| title | GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs |
| topic | Information Retrieval Digital Libraries |
| url | https://arxiv.org/abs/2511.11010 |