How are embeddings applied to text summarization?

Embeddings are numerical representations of text that capture semantic meaning, and they play a key role in modern text summarization systems. By converting words, sentences, or entire documents into dense vectors, embeddings enable algorithms to process and compare textual content mathematically. For example, a sentence embedding might represent its core ideas in a 300-dimensional vector, allowing similarity calculations between sentences. This is foundational for both extractive summarization (selecting key sentences) and abstractive summarization (generating new sentences). Tools like Word2Vec, GloVe, or transformer-based models (e.g., BERT) generate embeddings that capture context and relationships between terms, which helps identify central themes in a document.

In extractive summarization, embeddings help rank sentences by importance. One approach computes the embedding of each sentence and compares it to the document’s overall embedding (e.g., by averaging all sentence vectors). Sentences with higher cosine similarity to the document vector are deemed more central. For instance, a news article might have 20 sentences, and embeddings could highlight the five most representative ones. Libraries like spaCy or Sentence-BERT simplify this by providing pre-trained models to generate sentence embeddings. Another technique clusters sentence embeddings to group similar ideas and select exemplars from each cluster. This ensures the summary covers distinct topics without redundancy, such as summarizing a research paper by selecting one sentence from each methodology or results cluster.

For abstractive summarization, embeddings are used in sequence-to-sequence models like T5 or GPT. Here, the encoder converts input text into contextual embeddings, which the decoder uses to generate a condensed summary. For example, a model trained on news data might take a 500-word article and output a three-sentence summary. Fine-tuning these models with domain-specific data (e.g., medical reports) improves relevance. Additionally, embeddings help with attention mechanisms, allowing the model to focus on key phrases. For instance, in a legal document, embeddings of terms like “plaintiff” or “judgment” might receive higher attention weights during summary generation. Tools like Hugging Face’s Transformers library provide accessible APIs for experimenting with these techniques, making embeddings a practical tool for developers building summarization systems.