Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization

The development of an automatic evaluation metric remains an open problem in text generation. Widely used evaluation metrics, like ROUGE and BLEU, are based on exact word matching and fail to capture semantic similarity. Recent works, such as BERTScore, MoverScore and, Sentence Mover's Similarity, are an improvement over these standard metrics as they use contextualized word or sentence embeddings to capture semantic similarity. We in this work, propose a novel evaluation metric, Sentence Pair EmbEDdings (SPEED) Score, for text generation which is based on semantic similarity between sentence pairs as opposed to earlier approaches. To find semantic similarity between a pair of sentences, we obtain sentence-level embeddings from multiple transformer models pre-trained specifically on various sentence pair tasks such as Paraphrase Detection (PD), Semantic Text Similarity (STS), and Natural Language Inference (NLI). As these sentence pair tasks involve capturing the semantic similarity between a pair of input texts, we leverage these models in our metric computation. Our proposed evaluation metric shows impressive performance in evaluating both abstractive and extractive summarization models and achieves state-of-the-art results on the SummEval dataset, demonstrating the effectiveness of our approach. Also, we perform the run-time analysis to show that our proposed metric is faster than the current state-of-the-art.