METEOR (Metric for Evaluation of Translation with Explicit Ordering) is a metric used to measure the quality of candidate text based on the alignment between the candidate text and the reference text.
Key takeaways:
BERTScore uses BERT model embeddings to assess the semantic similarity between a reference and a candidate summary, focusing on meaning over exact word matches.
BERTScore calculation involves computing contextual embeddings, cosine similarity, and determining precision, recall, and F1 scores, along with importance weighting and rescaling for clarity.
The metric provides precision, recall, and F1 scores, offering insights into the alignment of the generated summary with the reference.
A high BERTScore indicates strong semantic alignment, while a low score suggests poor similarity, highlighting content weaknesses.
Evaluation metrics are the quantitative measures to evaluate the performance of machine learning models. Understanding these metrics is essential to measure the effectiveness of our model for particular tasks.
BERTScore is an evaluation metric that uses the BERT model to find the similarity between the reference and the candidate summary. It uses contextual embeddings from pretrained BERT models to compute the similarity between both. It assesses how well the meanings of words align rather than just focusing on exact word matches. BERTScore calculates precision, recall, and F1 score based on these semantic similarities, providing a more nuanced evaluation of text quality.
Following are the steps to calculate the BERTScore:
Computing contextual embedding
Computing cosine similarity
Computing precision, recall, and F1
Importance weighting
Rescaling
The first step for calculating the BERTScore is to compute the
Consider the contextual embedding for the candidate summary
After computing the contextual embedding for both summaries, the next step is to find their similarity. For this purpose, we compute the cosine similarity of contextual embedding vectors.
The cosine similarity between the reference summary token
Where
We used the pre-normalized vectors, so now the cosine similarity will be given as:
Calculating the BERTScore requires computing recall and precision. The recall is computed by comparing each token in the reference summary
The precision is computed by comparing each token in the candidate summary
The
We apply importance weighting to the BERTScore to emphasize important words and de-emphasize common ones. Inverse Document Frequency (IDF) can be incorporated into the BERTScore equations, although the effectiveness of this step can depend on both data availability and the specific domain of the text.
The cosine similarity score lies within the range
Where
After rescaling, its value will lie within the range
Now, let’s learn how to calculate the BERTScore score using Python:
Note: We used the
evaluate
package from the Hugging Face, which is widely used for evaluating the performances of the models. You can install this package using the commandpip3 install bert_score evaluate
. We have already installed this package for yow below.
from evaluate import load bertscore = load("bertscore") reference_summary = ["Machine learning is a subset of artificial intelligence"] predicted_summary = ["Machine learning is seen as a subset of artificial intelligence"] results = bertscore.compute(predictions=predicted_summary, references=reference_summary, model_type="distilbert-base-uncased") print("\nThe BERTScore for the predicted summary is given as: ", results)
The code above is explained in detail below:
Line 1: We import the load
library from the evaluate
package.
Line 2: We load the bertscore
library using the load
function.
Line 3: We define a reference_summary
variable and set its value to "Machine learning is a subset of artificial intelligence"
.
Line 4: We define a predicted_summary
variable and set its value to "Machine learning is seen as a subset of artificial intelligence"
.
Line 5: We used the compute()
function from the bertscore
library, which needs three parameters predictions
, references
, and the model_type
or lang
to compute the BERTScore for the predicted summary.
Line 6: We print the results
dictionary, which prints the precision, recall, F1 score, and library hash code for the provided predicted summary.
The final output of the BERTScore calculation consists of three primary metrics: precision, recall, and F1 score. These scores provide a comprehensive overview of how well the generated summary aligns with the reference summary, capturing both the accuracy and relevance of the information presented.
The high BERTScore indicates that the generated summary closely aligns with the reference summary in terms of meaning and context and the low BERTScore signifies that the generated summary lacks semantic similarity to the reference summary.
Ready to unlock the power of language processing? Getting Started with Google BERT will guide you through BERT’s architecture, transformer basics, and practical applications to excel in NLP tasks.
BERTScore represents a significant advancement in evaluating the quality of text summarization models by leveraging contextual embeddings from pretrained BERT models. This metric, based on precision, recall, and F1-score calculations, provides a robust measure of similarity between reference and candidate summaries. By incorporating importance weighting and rescaling techniques, BERTScore enhances its interpretability and relevance in assessing the fidelity of machine-generated summaries to their source texts. Its implementation through libraries like Hugging Face's evaluate
package simplifies integration into Python workflows, making it accessible for evaluating and improving natural language processing tasks.
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