Improving Summarization Factual Consistency with Natural Language Feedback

In this article, researchers explore ways to improve summarization by leveraging human feedback. They introduce three approaches – CUT (Continuous Updates and Testing), ROUGE (Revised Online Quality Estimation), and Maf (Multi-aspect Feedback) – that aim to refine the summarization process through iterative updates based on user input. These methods are designed to improve the accuracy and consistency of summaries by incorporating human feedback, which can help reduce the potential for errors or biases in large language models.
The authors demonstrate the effectiveness of these approaches using various experiments, showing that CUT can align LLMs with judgments annotated by humans or GPT4, while ROUGE focuses on improving the quality of summaries through self-refinement with human feedback. Maf, on the other hand, provides a more comprehensive framework for multi-aspect feedback, which can help improve the reasoning abilities of large language models.
The authors also discuss the limitations and challenges of these approaches, such as the need for high-quality annotations and the potential for overfitting or bias in the feedback process. They conclude by highlighting the importance of incorporating human feedback into the summarization process to improve its accuracy and consistency.
Overall, this article provides valuable insights into the use of human feedback in improving summarization, demonstrating the potential benefits of incorporating user input into the summarization process to enhance its quality and reliability.