EVA-Score: Evaluation of Long-form Summarization on Informativeness through Extraction and Validation

• URL: http://arxiv.org/abs/2407.04969v1
• Date: Sat, 6 Jul 2024 06:02:38 GMT
• Title: EVA-Score: Evaluation of Long-form Summarization on Informativeness through Extraction and Validation
• Authors: Yuchen Fan, Xin Zhong, Chengsi Wang, Gaoche Wu, Bowen Zhou,
• Abstract summary: EVA-Score is a new evaluation metric for long-form summarization. We show that our metric shows a state-of-the-art correlation with humans.
• Score: 19.80396362064475
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Summarization is a fundamental task in natural language processing (NLP) and since large language models (LLMs), such as GPT-4 and Claude, come out, increasing attention has been paid to long-form summarization whose input sequences are much longer, indicating more information contained. The current evaluation metrics either use similarity-based metrics like ROUGE and BERTScore which rely on similarity and fail to consider informativeness or LLM-based metrics, lacking quantitative analysis of information richness and are rather subjective. In this paper, we propose a new evaluation metric called EVA-Score using Atomic Fact Chain Generation and Document-level Relation Extraction together to automatically calculate the informativeness and give a definite number as an information score. Experiment results show that our metric shows a state-of-the-art correlation with humans. We also re-evaluate the performance of LLMs on long-form summarization comprehensively from the information aspect, forecasting future ways to use LLMs for long-form summarization.

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