Sentence Transformers can enhance text summarization and similarity evaluation by converting text into dense vector representations that capture semantic meaning. These models, trained to produce embeddings where similar sentences are close in vector space, enable efficient comparison of text segments. Here's how they apply to summarization and evaluation:
1. Text Summarization Support Sentence Transformers aid in both extractive and abstractive summarization. For extractive methods, embeddings can identify key sentences by measuring their similarity to the document's overall meaning. For example, a sentence embedding closely aligned with the average vector of the entire document likely represents a central idea. In abstractive summarization, embeddings help guide models like BART or T5 to generate summaries semantically aligned with the source. By fine-tuning on tasks like paraphrase generation, Sentence Transformers ensure generated summaries retain the original intent even when wording differs. For instance, a model could prioritize summary candidates whose embeddings have high cosine similarity with the source text’s embeddings.
2. Summary Quality Evaluation
Traditional metrics like ROUGE focus on lexical overlap but miss semantic nuances. Sentence Transformers address this by comparing summary and source embeddings. A high cosine similarity between the two vectors indicates the summary preserves the original meaning, even with different phrasing. For example, if a summary rephrases technical jargon into plain language, ROUGE might score poorly, but Sentence Transformers would recognize the semantic equivalence. This approach is particularly useful for evaluating abstractive summaries where paraphrasing is common. Developers can implement this by computing similarity scores via libraries like sentence-transformers, offering a complementary metric to ROUGE or BLEU.
3. Practical Implementation
A typical workflow involves using pre-trained models like all-mpnet-base-v2 to embed the original text and summary. For summarization, embeddings can rank sentences or guide beam search in generative models. For evaluation, a Python script using sentence_similarity() from the library directly compares embeddings. This method scales efficiently, as vector comparisons are computationally cheaper than processing full texts. However, results depend on the model’s training data—fine-tuning on domain-specific corpora (e.g., medical texts) may improve accuracy for specialized use cases. This approach balances speed and semantic awareness, making it viable for real-time applications like news aggregation or meeting note generation.