Automatic Text Summarization
The main aim of summarization technique is to showcase the core underlying topic in a dialogue .
Automatic summarisation is divided into
- Extractive
- Abstractive
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Data Science
Foundations of Large Language Models Course
Computing Sciences
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Evaluation Metric for Dialogue Summarization Tasks
Abstractive Text Summarization
Extractive Text Summarization
A language model is tasked with summarizing the following text:
Original Text: 'The company's quarterly report, released Tuesday, showed a 15% increase in revenue, driven primarily by strong sales in the European market. However, profits were down 5% due to rising operational costs and a one-time expenditure on new factory equipment. The CEO stated that while the profit dip is a concern, the strong revenue growth is a positive indicator for the future.'
The model produces two different summaries:
Summary A: 'The company's quarterly report showed a 15% increase in revenue. However, profits were down 5%. The strong revenue growth is a positive indicator for the future.'
Summary B: 'Despite a slight decrease in profits because of higher expenses, the company experienced significant revenue growth from its European sales, suggesting a promising outlook.'
Which of the following statements correctly analyzes the summarization methods used?
Choosing a Summarization Strategy for a Legal Tech Company
Examples of Constrained Prompts for Text Summarization
Match each automatic text summarization approach with the description of its core mechanism and primary trade-offs.
Analyzing Summarization Techniques