What is it about?
This paper introduces a new method for evaluating how well a summary captures the meaning of the original text. While current metrics like ROUGE compare summaries based on word overlap, our approach measures semantic similarity using sentence embeddings. This means it can better assess whether the summary conveys the same ideas as the original, even if the wording differs. Our tests show that this method aligns more closely with human judgment than ROUGE.
Featured Image
Photo by Nathan Dumlao on Unsplash
Why is it important?
Evaluating summaries is crucial for improving automatic summarization tools, but existing metrics like ROUGE often miss the nuances of meaning. Our proposed metric addresses this gap by focusing on semantic similarity, which is more reflective of how humans judge summaries. This advancement can lead to better summarization models and more reliable evaluations, benefiting applications like news aggregation, research literature reviews, and other areas where concise summaries are needed.
Perspectives
While working on this research, we realized that AI still falls short of matching the human ability to evaluate summaries, especially when it comes to understanding meaning rather than just matching words. This insight motivated us to develop a metric that better captures semantic similarity, bridging the gap between machine and human judgment. It was both challenging and rewarding to create a solution that moves beyond surface-level comparisons, and we hope this work encourages further advancements in making AI evaluation more aligned with human reasoning. Ultimately, we believe this is a step toward more trustworthy and meaningful automated text analysis.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
Read the Original
This page is a summary of: An Evaluation Metric for Assessing Summary-Level Semantic Similarity in Abstractive Text Summarization, February 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/aide64228.2025.10987460.
You can read the full text:
Contributors
The following have contributed to this page
