The choice between embedding document sections or whole documents depends on the use case and the granularity of information needed. For whole documents, strategies focus on capturing broad themes, while section embeddings prioritize localized context. Here’s how to approach each effectively:
Embedding Whole Documents When embedding entire documents, the goal is to represent the overarching topic or purpose. One common strategy is to use models designed for longer texts, such as doc2vec or transformer-based models with techniques to handle token limits. For example, if using BERT (limited to 512 tokens), you might split the document into chunks, embed each chunk, and average the results. Alternatively, models like Longformer or Reformer, which handle longer sequences, can process entire documents without splitting. Preprocessing steps like removing irrelevant content (footers, boilerplate) or summarizing key sections (abstracts, conclusions) can help focus the embedding on core themes. For instance, embedding a research paper by averaging its introduction, methodology, and conclusion vectors might better represent its overall contribution than embedding raw, unfiltered text.
Embedding Document Sections Sections require embeddings that capture specific contexts. Sentence-transformers like SBERT are ideal here, as they excel at encoding shorter, focused text. For example, in a technical manual, embedding each section (e.g., "Installation," "Troubleshooting") separately allows precise retrieval of relevant steps. Including metadata (section headers, paragraph numbers) as part of the input text can enhance context. Fine-tuning the embedding model on domain-specific data (e.g., legal clauses or medical reports) further improves accuracy. If sections are logically divided (e.g., by headings), splitting documents using rule-based methods or NLP libraries (spaCy, NLTK) ensures meaningful chunks. For a FAQ page, embedding each question-answer pair individually enables matching user queries to specific answers rather than the entire page.
Trade-offs and Use Cases Whole-document embeddings are efficient for tasks like topic classification or coarse retrieval but may miss nuanced details. Section embeddings are better for pinpointing information, like finding a specific clause in a contract. Hybrid approaches can balance both: for example, using whole-document embeddings to filter candidate documents in a search pipeline, then reranking with section-level embeddings for precision. Tools like FAISS or Annoy can index embeddings for fast retrieval. The key is aligning the strategy with the application—use whole documents for broad categorization and sections for granular, context-aware tasks.