Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering

Abstract: This study presents a controllable abstract summary generation method for LLMs based on prompt engineering. To address the issues of summary quality and controllability in traditional methods, we design a multi-stage prompt generation framework. This framework generates summaries with varying levels of abstraction by performing semantic analysis, topic modeling, and noise control on the input text. The experiment uses the CNN/Daily Mail dataset and provides a detailed analysis of different prompt lengths, data noise, and text types. The experimental results show that prompt length has a significant impact on the quality of generated summaries. Both very short and very long prompt tokens result in a decrease in summary quality. Data noise also negatively affects the summary generation process. As noise levels increase, the ROUGE-L score gradually decreases. Furthermore, different text types have varying effects on the model's ability to generate summaries. The model performs best when handling news texts, while its performance is worse when processing academic articles. This research provides new insights into improving summary generation using LLMs, particularly in how controlling prompt strategies and optimizing text preprocessing can enhance summary accuracy and controllability.

• The paper introduces a multi-stage prompt engineering framework that enables controllable abstraction levels in automatic summary generation.
• It leverages semantic analysis, reinforcement learning, and multi-task learning to optimize prompts with ideal lengths of 30-40 tokens.
• Experimental results on the CNN/Daily Mail dataset show superior performance with enhanced ROUGE and BLEU metrics, confirming its adaptability.

Controllable Abstraction in Summary Generation for LLMs via Prompt Engineering

Introduction

The paper "Controllable Abstraction in Summary Generation for LLMs via Prompt Engineering" (2510.15436) introduces a novel framework designed to address the dual challenges of abstraction control and summary quality in the domain of automatic text summarization using LLMs. By leveraging prompt engineering, the authors propose a multi-stage prompt generation approach that offers significant improvements in generating summaries with varying abstraction levels. This work advances summarization tasks by enhancing user-oriented customization and adaptability, a critical requirement given diverse application demands across domains like news, finance, and healthcare.

Methodology

The researchers present a task-driven, multi-stage prompt generation framework that constitutes the backbone of their methodology. The process begins with semantic analysis and topic modeling of input texts, using these analyses to automatically craft targeted prompts. These prompts guide LLMs to generate summaries aligned with desired abstraction levels. The framework's design involves a multi-level objective function that balances semantic alignment, abstraction level, and contextual adaptability of prompts. Reinforcement learning further refines prompt optimization by evaluating the generated summaries with a reward function. The method also incorporates multi-task learning to optimize the prompt generation processes for various tasks, enhancing the adaptability and performance of LLMs in summarizing diverse text types.

Experimental Evaluation

The evaluation employs the CNN/Daily Mail dataset, a standard benchmark in summarization research owing to its comprehensive selection of news articles and human-generated summaries. In comparative experiments, the proposed method demonstrates superior performance in capturing key information and maintaining coherence in summaries, as evidenced by prominent metrics such as ROUGE-N, ROUGE-L, and BLEU. For instance, the method achieves a ROUGE-N score of 0.50, outperforming alternatives like DeepExtract and WhisperSum. The study further investigates the effects of prompt length, data noise, and text type on summary quality. Optimal results occur with prompt lengths of 30 to 40 tokens, highlighting the importance of context adequacy. Data noise negatively impacts summary quality, underscoring the need for precise noise management strategies. Diverse text types yield varying results, with news articles showcased as the easiest to summarize due to their structured format and consistent style.

Implications

The research provides valuable insights into the field of natural language processing by elucidating the influence of prompt engineering on abstraction control in summary generation. By enhancing LLM adaptability through this controllable mechanism, the methodology extends the application boundaries of LLMs from being mere generative models to advanced tools capable of tailoring outputs to specific user and task requirements. This adaptability proves beneficial in specialized domains where controlled summarization is paramount, such as generating concise legal document summaries or detailed financial report analyses.

Conclusion

The paper presents a robust framework that not only improves summary quality and controllability but also sets the groundwork for future exploration in the domain. As text data complexity and volume surge, ongoing efforts to enhance model adaptability, particularly for intricate and noisy inputs, are crucial. Future research directions proposed include evaluating the method across various text domains, leveraging large-scale unsupervised training, and integrating advanced methodologies like multimodal and transfer learning. These enhancements promise to fortify the robustness and flexibility of LLMs, paving the way for broader application and efficacy in complex natural language processing tasks.