Summarization
AssemblyAI's Summarization model provides a powerful tool for quickly distilling important information from an audio file.
Getting started
In the Summarizing virtual meetings guide, the client uploads an audio file and configures the API request to create a summary of the content. By processing the audio, the Summarization model generates a brief yet informative overview that highlights the essential points of the recording.
You can also view the full source code here.
Types and models
The tables below show which summary types and summary models are compatible as well as their best use cases.
bullets (default) | A bulleted summary with the most important points. | - The human brain has nearly tripled in mass in two million years. - One of the main reasons that our brain got so big is because it got a new part, called the frontal lobe. |
bullets_verbose | A longer bullet point list summarizing the entire transcription text. | Dan Gilbert is a psychologist and a happiness expert. His talk is recorded live at Ted conference. He explains why the human brain has nearly tripled in size in 2 million years. He also explains the difference between winning the lottery and becoming a paraplegic. - In 1994, Pete Best said he's happier than he would have been with the Beatles. In the free choice paradigm, monet prints are ranked from the one they like the most to the one that they don't. People prefer the third one over the fourth one because it's a little better. - People synthesize happiness when they change their affective. Hedonic aesthetic to make up your mind and change your mind is the friend of natural happiness. But it's the enemy of synthetic happiness. The psychological immune system works best when we are stuck. This is the difference between dating and marriage. People don't know this about themselves and it can work to their disadvantage. - In a photography course at Harvard, 66% of students choose not to take the course where they have the opportunity to change their mind. Adam Smith said that some things are better than others. Dan Gilbert recorded at Ted, 2004 in Monterey, California, 2004. |
gist | A few words summarizing the entire transcription text. | A big brain |
headline | A single sentence summarizing the entire transcription text. | The human brain has nearly tripled in mass in two million years. |
paragraph | A single paragraph summarizing the entire transcription text. | The human brain has nearly tripled in mass in two million years. It went from the one-and-a-quarter-pound brain of our ancestor, habilis, to the almost three-pound meatloaf everybody here has between their ears. |
informative (default) | Best for files with a single speaker such as presentations or lectures | bullets, bullets_verbose, headline, or paragraph | punctuate and format_text set to true |
conversational | Best for any 2 person conversation such as customer/agent or interview/interviewee calls | bullets, bullets_verbose, headline, or paragraph | punctuate, format_text and speaker_labels or dual_channel set to true |
catchy | Best for creating video, podcast, or media titles | headline, or gist | punctuate and format_text set to true |
Understanding the response
The most important attribute in the response object is the summary key. It contains the summary of the audio file you submitted, and can be accessed directly from the response.
Troubleshooting
You can use the summary_model and summary_type parameters to customize the output. See the tables in the Best practices section for more information on the available options.
No, you cannot have both Auto Chapters and Summarization active in the same request. If you enable both models in a single request, you will receive an error message.
The inference speed of the Summarization model depends on the desired output length. However, a single batch can be processed in less than 1 second.
No, Summarization only generates a single abstractive summary of the entire audio file, and does not provide word-level information or speaker labels. If you need word-level information, consider using AssemblyAI's Speech Recognition or Speaker Diarization models instead.