Summarization Metric

Summarization metrics aim to automatically assess the quality of generated summaries, enabling efficient comparison of different summarization systems. Current research emphasizes improving metric accuracy by incorporating factors like faithfulness, user needs (including diverse perspectives and expertise levels), and discourse structure, often leveraging large language models (LLMs) and graph neural networks (GNNs) for improved performance. These advancements are crucial for advancing summarization technology across diverse domains, from scientific literature to medical records and legal documents, ultimately leading to more effective and reliable automated summarization tools.