--- title: Enabling Description of Under-Described Archival Video Collections through AI description: AI is transforming the description of archival videos, automating metadata creation, enhancing discoverability, and supporting archivists while maintaining professional judgment and ethical practices. image: https://insights.aviaryplatform.com/hubfs/AviaryDiscoverabilityGap-Cropped.png --- [![Aviary](https://www.aviaryplatform.com/na_images/aviary-logo.png)](https://www.aviaryplatform.com) [Discover](https://www.aviaryplatform.com/catalog?search_field=advanced&search_type=simple&title_text=&type_of_search[]=simple) [Pricing](https://www.aviaryplatform.com/pricing) [Features](https://www.aviaryplatform.com/features) [Case Studies](https://www.aviaryplatform.com/case_studies) [Insights](https://insights.aviaryplatform.com/aviary-insights) [About](https://www.aviaryplatform.com/about) [Discover](https://www.aviaryplatform.com/catalog?search_field=advanced&search_type=simple&title_text=&type_of_search[]=simple) [Pricing](https://www.aviaryplatform.com/pricing) [Features](https://www.aviaryplatform.com/features) [Case Studies](https://www.aviaryplatform.com/case_studies) [Insights](https://insights.aviaryplatform.com/aviary-insights) [About](https://www.aviaryplatform.com/about) [Skip to content](https://insights.aviaryplatform.com/aviary-insights/description-through-ai#main-content) ![Visualization of a library with disorganized and hard-to-find av assets](https://insights.aviaryplatform.com/hs-fs/hubfs/AviaryDiscoverabilityGap-Cropped.png?width=957&height=611&name=AviaryDiscoverabilityGap-Cropped.png) Aviary audiovisual archives # Enabling Description of Under-Described Archival Video Collections through AI ![Kevin Glick](https://insights.aviaryplatform.com/hs-fs/hubfs/4562_zucker20211026.jpg?width=48&height=48&name=4562_zucker20211026.jpg) Kevin Glick December 18, 2025 *This blog post summarizes a recent presentation at the Association of Moving Image Archivists' [AMIA 2025 conference](https://amiaconference.net/amia-2025-program/) in Baltimore.*  Audiovisual archives hold incredible cultural and historical value, but a significant challenge has always been **discoverability**. Traditional methods of creating descriptive metadata for video content are time-consuming, manually intensive, and often result in sparse data. This leaves thousands of hours of rich video content almost invisible to researchers. Our goal is to automatically generate useful, time-coded, and highly accurate metadata to unlock the immense value hidden within these large video collections. We believe that emerging AI technologies can support a scalable, cost-effective solution for describing archival video collections that currently lack sufficient metadata.AI: A Practical Tool, Not a Magic Wand We view AI as a tool to **augment professional archival practice, not replace it.** The value of archival description remains rooted in professional judgment—contextual interpretation, ethical considerations, and cultural sensitivity. However, AI can bridge the gap created by limited staff resources by: - **Automating** the creation of baseline descriptive metadata. - Improving **search and discovery** across large holdings. - **Reducing manual labor** on repetitive tasks. - Supporting **accessibility** improvements. It's essential to frame this work within responsible and ethical practice: - **Transparency:** Be clear about when and how AI-generated metadata is created. - **Accuracy and Human Review:** Archivists and subject-matter experts must evaluate AI outputs and refine them, as even the best tools produce errors. - **Bias:** Be intentional about bias, as AI systems inherit the biases of their training data. Archivists should consider when *not* to use automated tools, especially with sensitive material. Aviary's 3  different approaches to AI description The Aviary team has investigated and implemented three complementary approaches to utilizing AI for video collection description: 1. Named Entity Recognition (NER) with spaCy - **Method:** This approach runs on an existing transcript, using the **spaCy Python library** to analyze text, identify entities (Person, Place, Date, Org, Event), and classify them. It then automatically matches these entities to **Wikidata** entries to extract authoritative entity names, descriptions, images, dates, and URLs. - **Benefit:** It transforms unstructured text into structured, linkable, and contextually rich metadata. It's fast, efficient, and great for fine granularity. - **Best Suited For:** Video with high-quality transcripts that mention many named people, places, events, and dates. 2. Large Language Models (LLMs) Analyzing Audio with AssemblyAI - **Method:** This uses **AssemblyAI** to analyze video with audio content, automatically dividing the content into logical segments and providing a summary for each—or summarizing the entire video. - **Benefit:** It does not require an existing transcript, produces high-quality output easily consumed by researchers, and enhances searchability and video navigation. It automates the time-consuming manual task of creating a table of contents. - **Best Suited For:** Video with varied content and many different segments where an existing transcript may not be available. 3. LLMs Analyzing Video with Cloudglue - **Method:** **Cloudglue** preprocesses the video's visuals, audio, and on-screen text into structured, machine-readable data. An LLM then analyzes this data to generate file-level descriptive metadata, including **Summary, Keywords, Table of Contents, Speakers, Languages, and Sensitive Content.** - **Benefit:** This is the base layer of metadata creation and a crucial starting point. Importantly, it **does not require video with speech or sound**, making it ideal for silent or visually-focused archival footage. - **Best Suited For:** Video with more singular subject matter or fewer segments, where visual analysis is key. Key Takeaways The investigation into these AI tools has led to three core conclusions: 1. **AI tools are practical and scalable solutions for under-described videos.** They help eliminate backlogs and surface previously hidden collections. 2. **Different approaches to AI description address complementary aspects of audiovisual content.** Archivists can select the best tool based on the material's characteristics (e.g., presence of speech, visual complexity, need for high-granularity metadata). 3. **Professional cataloging remains essential.** AI is a powerful assistant, but the critical work of contextual interpretation, quality control, and long-term metadata stewardship remains firmly in the hands of archivists. Ready to transform your under-described video collections and break through your metadata backlog? **Explore these AI-powered description services in the Aviary platform** and start unlocking the value of your archives. To learn more about implementing NER, AssemblyAI, and Cloudglue workflows, or to schedule a demonstration, visit [aviaryplatform.com](https://aviaryplatform.com) today.   ## Share this post [mailto:https%3A%2F%2Finsights.aviaryplatform.com%2Faviary-insights%2Fdescription-through-ai](mailto:https%3A%2F%2Finsights.aviaryplatform.com%2Faviary-insights%2Fdescription-through-ai) ## Keep reading ### [![Illustration of the Aviary transcript editor and a transcriptionist sitting at a computer desk with a foot peadal](https://insights.aviaryplatform.com/hs-fs/hubfs/TranscriptionPedals.png?width=2240&height=1260&name=TranscriptionPedals.png) Aviary AV captions Configuring a foot pedal for Aviary transcript and index editing](https://insights.aviaryplatform.com/aviary-insights/foot-pedal) ### [Aviary audiovisual AV A Winning Partnership for NEH Collections Stewardship Grants: George Blood LP & Aviary](https://insights.aviaryplatform.com/aviary-insights/neh-stewardship-grants) [See all Aviary Insights](https://insights.aviaryplatform.com/aviary-insights) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Kevin Glick", "url" : "https://insights.aviaryplatform.com/aviary-insights/author/kevin-glick" }, "dateModified" : "2025-12-18T02:23:59.372Z", "datePublished" : "2025-12-18T02:23:59.000Z", "headline" : "Enabling Description of Under-Described Archival Video Collections through AI", "image" : [ "https://insights.aviaryplatform.com/hubfs/AviaryDiscoverabilityGap-Cropped.png" ], "mainEntityOfPage" : { "@id" : "https://insights.aviaryplatform.com/aviary-insights/description-through-ai", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://insights.aviaryplatform.com/hubfs/aviary%20by%20avp%20logo%20900%20x%20400.png" }, "name" : "Aviary Platform - Audio Visual Preservations Solutions Inc" } } ```