AssemblyAI's Summarization model provides a powerful tool for quickly distilling important information from an audio file.
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 explore the full JSON response here:
Note that Auto Chapters and Summarization cannot both be used in the same request.
Understanding the response
The JSON object above contains all information about the transcription. Depending on which Models are used to analyze the audio, the attributes of this object will vary. For example, in the quickstart above we did not enable Entity Detection, which is reflected by the
entity_detection: false key-value pair in the JSON above. Had we activated Entity Detection, then the
entitieskey value would contain the file summary (and additional details) rather than the current
To access the Summarization information, we use the
The reference table below lists all relevant attributes along with their descriptions, where we've called the JSON response object
results. Object attributes are accessed via dot notation, and arbitrary array elements are denoted with
results.words[i].text refers to the
text attribute of the
i-th element of the
words array in the JSON
|boolean||Whether Summarization was enabled in the transcription request|
|string||The summary of the audio file|
|string||The type of summary selected for output (see below for additional details)|
|string||The summarization model selected (see below for additional details)|
Types and models
The tables below show which summary types and summary models are compatible as well as their best use cases.
If you specify one of
summary_type, you also need to specify the other.
|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.
|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.|
|A few words summarizing the entire transcription text.||A big brain|
|A single sentence summarizing the entire transcription text.||The human brain has nearly tripled in mass in two million years.|
|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.|
|Best for files with a single speaker such as presentations or lectures|
|Best for any 2 person conversation such as customer/agent or interview/interviewee calls|
|Best for creating video, podcast, or media titles|
No, you can't have both Auto Chapters and Summarization active in the same request. If you enable both models in a single request, you'll 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 doesn't provide word-level information or speaker labels. If you need word-level information, consider using AssemblyAI's Speech Recognition or Speaker Diarization models instead.