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Answer: Fine-tune the model on a dataset of news articles and their summaries to improve its understanding of the summarization task.
To generate automated summaries of news articles using an Azure OpenAI model, fine-tuning the model on a dataset of news articles and their summaries (C) is the most effective approach. This allows the model to learn the patterns and techniques required for summarization. Using a single, broad prompt (A) may not provide enough guidance for the model to generate focused summaries. Implementing a text preprocessing system (B) can be helpful but does not directly address the need for training the model on the summarization task. Limiting the model's summaries to a predefined set of topics or keywords (D) may restrict its ability to handle a wide range of articles.
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You are working on a project to generate automated summaries of news articles using an Azure OpenAI model. The model should be able to understand the main points and key information in the articles and generate concise summaries. Which of the following techniques should you consider implementing to achieve this?
A
Use a single, broad prompt for all articles to maintain consistency in summaries.
B
Implement a text preprocessing system to extract key information and main points from the articles.
C
Fine-tune the model on a dataset of news articles and their summaries to improve its understanding of the summarization task.
D
Limit the model's summaries to a predefined set of topics or keywords.
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