About the Summarization Models in Generative AI
A version of the cohere.command pretrained model is available in OCI
Generative AI for text summarization. The summarization model is
the same as one of the pretrained text generation models, but it has parameters that you can
specify for text summarization. Use the summarization model for any text that you want to see a
summary of. Input text and get important information out of that text.
The following categories are ideal text sources for summarization:
- News articles
- Blog posts
- Chat transcripts
- Scientific articles
- Meeting notes
- Product reviews
Summarization Model Parameters
When using the summarization model in the playground, you can get a different output by changing the following parameters.
- Length
-
The approximate length of the summary. You can choose short, medium, or long. Short summaries are roughly up to two sentences long, medium summaries are between three and five sentences, and long summaries might have six or more sentences. For the Auto value, the model chooses a length based on the input size.
- Format
-
Whether to display the summary in a free-form paragraph or in bullet points. For the Auto value, the model chooses the best format based on the input text.
- Extractiveness
-
How much to reuse the input in the summary. Summaries with high extractiveness tend to use sentences verbatim, and summaries with low extractiveness tend to paraphrase.
- Temperature
-
The level of randomness used to generate the output text.
Tip
To summarize a text, start with the temperature set to 0. If you don't require random results, we recommend a temperature value of 0.2. Use a higher value if, for example, you plan to select various summaries afterward. However, don't use a high temperature for summarization because a high temperature encourages the model to produce creative text, which might also include hallucinations and factually incorrect information. - Additional command
-
Other summarizing options such as style or focus. Write one or more additional commands in a natural language as instructions to the model, for example, "focus on dates", or "write in a conversational style", or "end the resume with END SUMMARY".