SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization

2025-02-01 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Emerging Technologies & & Innovation · Depth: Expert, extended

Summary

SCURank is a novel framework designed to enhance summarization by ranking multiple candidate summaries based on their information richness and semantic importance, rather than unstable LLM comparisons or surface-level metrics like ROUGE. It addresses the limitations of existing LLM-based ranking strategies, which suffer from inconsistency and positional bias, and traditional metrics that are insufficient for high-quality summaries. SCURank operates in three stages: extracting Summary Content Units (SCUs) using gpt-4o-mini, aggregating these SCUs via HDBSCAN clustering to estimate importance, and scoring each summary by summing its SCU importance, normalized by length. Experimental results on CNN/DailyMail and XSum datasets demonstrate that SCURank outperforms GPTRank and other baselines, improving distilled model performance and abstractiveness, especially when trained with diverse summaries from multiple LLMs.

Key takeaway

For AI engineers and research scientists developing summarization models, SCURank offers a more stable and semantically meaningful approach to ranking candidate summaries than traditional metrics or direct LLM-based methods. Integrating SCURank into contrastive learning frameworks like BRIO can significantly improve the performance and abstractiveness of distilled summarization models, especially when leveraging diverse outputs from multiple LLMs. Consider adopting SCURank to enhance the quality and robustness of your summarization model training pipelines.

Key insights

SCURank leverages Summary Content Units (SCUs) and clustering to robustly rank summaries by information richness, improving distillation.

Principles

• Information retention is core to summarization evaluation.
• Diverse LLM-generated summaries enhance abstractiveness.
• LLM-based ranking is prone to instability and bias.

Method

SCURank extracts SCUs using gpt-4o-mini, clusters them with HDBSCAN based on semantic similarity, and scores summaries by summing normalized SCU importance, then ranks them.

In practice

• Use gpt-4o-mini for cost-effective SCU extraction.
• Employ HDBSCAN for density-based clustering of SCUs.
• Normalize summary scores by length to mitigate bias.

Topics

• SCURank
• Summary Content Units
• Model Distillation
• Contrastive Learning
• Abstractive Summarization

Code references

• IKMLab/SCURank
• meta-llama/llama3

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.