In what ways can Sentence Transformers assist in text summarization tasks or in evaluating the similarity between a summary and the original text?

Sentence Transformers can assist in text summarization tasks by generating dense vector representations (embeddings) of text that capture semantic meaning. These embeddings enable algorithms to identify key sentences or phrases in a document that best represent its overall content. For example, in extractive summarization, each sentence in the original text can be embedded, and the sentences closest to the document’s average embedding (or other criteria like diversity) are selected to form the summary. In abstractive summarization, embeddings can guide generative models to produce summaries that align semantically with the original text, ensuring coherence and relevance.

For evaluating similarity between a summary and the original text, Sentence Transformers compute embeddings for both and measure their closeness in vector space. Metrics like cosine similarity or Manhattan distance quantify how well the summary captures the source’s meaning. For instance, a high cosine similarity between the summary’s embedding and the original text’s embedding suggests strong semantic overlap. Additionally, cross-encoder models (a type of Sentence Transformer) can directly compare text pairs by processing them together, providing fine-grained similarity scores. This is useful for detecting factual consistency, such as verifying if specific claims in the summary align with the original.

Practical implementations might involve using pre-trained models like all-MiniLM-L6-v2 for efficient embedding extraction. For extractive summarization, a developer could split the original text into sentences, embed them, compute the average document embedding, and select sentences with the highest similarity to this average. To evaluate a summary, embeddings for the summary and original text could be compared using cosine similarity via libraries like sentence-transformers. For deeper analysis, a cross-encoder model could check individual summary claims against the original text, flagging mismatches. These approaches provide measurable, scalable ways to automate summarization and quality assessment without relying on manual evaluation.