Automatically summarize audio and video files at scale with AI summarization

The amount of audio and video content available online has been increasing at an exponential rate4a trend supported by recent data shows that 86% of U.S. adults get news from digital devices. This creates both a challenge and an opportunity for product teams building with audio/video. On one hand, there's a tremendous amount of information encoded into audio/video files that can be leveraged to build powerful features and products. On the other hand, it can be difficult for product teams to work with audio/video data in its default state.

This guide explores how text summarization transforms audio and video content into actionable insights, covering the technical approaches, business benefits, and practical implementation strategies that leading companies use to build Voice AI-powered summarization at scale.

What is text summarization and why it matters for audio and video content

Text summarization is an AI process that automatically condenses lengthy text into concise summaries containing the most critical information. For audio and video content, this means converting hours of recordings into actionable insights1key points, decisions, and topicswithout manual review.

The real power comes when you combine AI speech-to-text models with summarization capabilities. Instead of just converting audio to text, you're creating structured information that teams can actually usewhether that's generating meeting notes, creating podcast highlights, or extracting customer insights from thousands of support calls.

Types of text summarization approaches

Two primary AI approaches power text summarization:

Extractive summarization:

• Pulls key sentences directly from original text
• Ensures factual accuracy by using exact wording
• Best for compliance monitoring and generating quotes

Abstractive summarization:

• Creates new sentences that capture meaning and context
• Produces more natural, human-like summaries
• Ideal for meeting notes, headlines, and content descriptions

AssemblyAI's summarization models leverage abstractive techniques to produce more human-like, coherent summaries for any audio or video file. This gives developers the flexibility to choose summary formats that best fit their use case.

Text summarization models in action

AI technology continues to rapidly advance, making it easier to leverage AI across industries. For example, users can now generate high-quality, realistic images and art with a single text prompt. Transformers have taken the world by storm and opened up the opportunity for Large Language Models (LLMs) that can generate code and write articles.

AssemblyAI's AI Summarization models, for example, are built on the same AI technologyTransformersthat are behind most of these advances. The Summarization models are also purpose-built to work well on conversational data (phone calls, zoom meetings, screen recordings, videos, etc.).

To demonstrate the power of these models, see the number of examples below:

Business benefits and ROI of Voice AI summarization

AI summarization delivers measurable business returns, and research indicates it can produce an ROI of over 395% for end users across four key areas:

• Operational efficiency: Automate manual call reviews and QA workflows, with a 2023 study finding that AI assistance increases contact center agent productivity by 14% on average.
• Business intelligence: Analyze 100% of conversations to identify trends and insights, a practice that is becoming widespread as an industry survey found 76% of companies embed conversation intelligence in over half their customer interactions.
• User experience: Transform hours of content into scannable highlights
• Product velocity: Build differentiated features without maintaining AI infrastructure. A survey of tech leaders confirms that partnering with an AI provider allows businesses to focus on innovation and achieve a faster time-to-market.

Companies in the conversational intelligence space use summarization to help their customers accelerate QA workflows and reduce time spent on post-call work. When representatives no longer need to manually summarize every interaction, they can handle more conversations and focus on building customer relationships. In fact, research shows AI tools help agents with only two months of tenure perform as well as agents with more than six months of experience.

Media platforms use comprehensive summarization to understand content patterns across thousands of hours of recordingsinsights that would be impossible to gather manually. Virtual meeting platforms transform hour-long recordings into scannable highlights that users actually read.

Startups like Supernormal and Circleback AI leverage pre-built summarization models to compete with established players. They focus their resources on unique product experiences rather than foundational AI infrastructure.

A deep dive into how AssemblyAI's text summarization models work

AssemblyAI's text summarization models are fast, scalable, and continuously updated by an in-house team of AI experts to keep it state-of-the-art as new research emerges. These models are accessible through a single API call, making it easy for teams of all sizes to embed the models into their products.

For example, you can easily follow along to learn how to request a summary for an audio file using the AssemblyAI Python SDK here.

As you'll see in the example, developers have access to customizable summary formats, which are controlled by using the summary_model and summary_type parameters together. This offers developers the flexibility and control they need to generate different types of summaries depending on their use case.

For a more detailed guide on how to use the AssemblyAI Python SDK, visit this tutorial.

Here is a description of the available summary types AssemblyAI offers:

Here is a description of the available summary models and types AssemblyAI offers. For the most up-to-date information, please see the official Summarization documentation.

Summary Models

The summary model determines the style and tone of the summary.

Summary Types

The summary type determines both the length and the format of the summary.

Use cases for summarization

AI text summarization models and AI Summarizers for audio and video help customers across a wide range of industries and use cases, including conversational intelligence, video/media platforms, podcasts, and virtual meeting platforms.

Conversational Intelligence

A call center application

AI summarization can be a powerful addition to conversation intelligence tools and help deliver substantial ROI for end users. Companies like CallSource and Global use summarization to transform their call analytics capabilities.

Top benefits of text summarization for conversation intelligence include:

• Automating manual call review to speed up QA workflows
• Monitoring all calls for key insights or potential areas of concern
• Automating notetaking to increase representative and customer engagement
• Enabling more efficient context sharing between teams
• Identifying key trends over time

Video/Media Platforms

A video/media platform

AI text summarization can help video and media platforms build tools that distill long educational courses, lectures, media broadcasts, and more into their most essential points for faster consumption. Companies like Veed and Descript use these capabilities to enhance their content creation workflows.

Summarization tools can also facilitate easier collaboration between viewers and make it easier for additional conversational intelligence analysis tools to be applied to the summary.

Podcasts

A podcast platform

When integrated into a podcasting platform, text summarization tools can give podcast listeners a quick summary of what the podcast is about before they listen, making the UI more intuitive for users. Platforms like Podchaser leverage these capabilities to improve content discovery.

AI summarization can also make podcast episodes more searchable for end users and enhance ad targeting for podcasting platforms by allowing them to serve ads that more closely mirror the listeners' current interests.

Virtual Meeting Platforms

A virtual meeting platform

Reflecting a growing business trend in which virtual meeting use increased to 77% by 2022, virtual meeting platforms and AI voice assistants like Fireflies integrate Speech AI and AI summarization to offer summaries of full meetings, make meeting recordings easier to consume, and readily identify key takeaways and post-call action items. Companies like Supernormal and Circleback AI have built entire businesses around this capability.

Some virtual meeting platforms also add shareable video clips that correspond to key moments in the summary.

These virtual meeting tools integrate into popular meeting platforms like Google Meet, Zoom, and Microsoft Teams for ease of use for the end-user.

Implementation strategies for audio and video summarization at scale

Deploy audio and video summarization in four strategic steps:

1. Define objectives: Identify specific problems like automating meeting notes or creating content previews
2. Ensure accurate transcription: High-quality summaries require precise speech-to-text conversion, and industry research shows that product teams rank quality (58%) and accuracy (47%) as top factors when choosing an AI vendor.
3. Select summary format: Choose between paragraphs, bullet points, headlines, or custom formats
4. Test and scale: Validate with real data, then expand based on user feedback and usage patterns

Companies building in production prioritize accuracy in transcriptionif the transcript misses key information, the summary will too. Speech understanding models let you specify summary format with a single parameter, giving you flexibility without complexity.

Once you've validated your approach, scaling becomes straightforward. Modern APIs handle millions of hours of audio without requiring infrastructure changes on your end.