The development of machine learning has led to the rapid growth of technological fields such as Natural Language Processing (NLP) and Large Language Models (LLMs). However, with the advancement of these fields, a new problem has emerged: How reliable are the accuracy of evaluation metrics? In this context, BertScore has emerged as a significant metric that has come forward as an alternative to traditional evaluation metrics.
Text summarization is condensing a lengthy text into a shorter and more concise version. This process highlights the text's key points and makes it easier for the reader to understand quickly. BertScore is a method used to measure the quality of text summarization. This method measures how similar the text summary is to the original text.
BertScore addresses two common issues that n-gram-based metrics often encounter. First, n-gram models tend to incorrectly match paraphrases because semantically accurate expressions may differ from the surface form of the reference text, which can lead to incorrect performance estimation. BertScore, on the other hand, performs similarity calculations using contextualized token embeddings shown to be effective for entailment detection. Second, n-gram models cannot capture long-range dependencies and penalize semantically significant reordering.
For more click on the medium link