UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning

• URL: http://arxiv.org/abs/2503.11118v1
• Date: Fri, 14 Mar 2025 06:29:51 GMT
• Title: UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning
• Authors: Kristin Qi, Youxiang Zhu, Xiaohui Liang,
• Abstract summary: We present our approach to the PerAnsSumm Shared Task, which involves perspective span identification and perspective-aware summarization.<n>For span identification, we adopt ensemble learning that integrates three transformer models through averaging to exploit individual model strengths.<n>For summarization, we design a suite of Chain-of-Thought (CoT) prompting strategies that incorporate keyphrases and guide information to structure summary generation into manageable steps.
• Score: 8.095763327154335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present our approach to the PerAnsSumm Shared Task, which involves perspective span identification and perspective-aware summarization in community question-answering (CQA) threads. For span identification, we adopt ensemble learning that integrates three transformer models through averaging to exploit individual model strengths, achieving an 82.91% F1-score on test data. For summarization, we design a suite of Chain-of-Thought (CoT) prompting strategies that incorporate keyphrases and guide information to structure summary generation into manageable steps. To further enhance summary quality, we apply prompt optimization using the DSPy framework and supervised fine-tuning (SFT) on Llama-3 to adapt the model to domain-specific data. Experimental results on validation and test sets show that structured prompts with keyphrases and guidance improve summaries aligned with references, while the combination of prompt optimization and fine-tuning together yields significant improvement in both relevance and factuality evaluation metrics.

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