The choice between embedding models for short text versus long documents depends on how each handles context, semantic density, and computational efficiency. For short text (e.g., search queries, product titles), models optimized for sentence-level semantics like Sentence-BERT (SBERT) or Universal Sentence Encoder (USE) work best. These focus on capturing meaning in condensed phrases without requiring deep document-wide context. For long documents (e.g., research papers, legal contracts), models like BERT with pooling strategies, Doc2Vec, or specialized variants like Longformer are more effective, as they aggregate information across paragraphs or handle extended token sequences.
Short text benefits from models that prioritize precise semantic relationships in limited words. For example, SBERT fine-tunes BERT using siamese networks to create embeddings optimized for comparing sentence pairs, making it ideal for tasks like clustering product descriptions or matching search queries. Similarly, USE (available in TensorFlow Hub) is trained on conversational data and web text, excelling at capturing intent in phrases like chatbot inputs. These models avoid diluting meaning by treating short text as a single unit rather than breaking it into fragments. However, they may struggle with rare terms or ambiguous phrasing in isolation, which is why domain-specific fine-tuning (e.g., using a custom dataset of support tickets) often improves results.
Long documents require models that retain coherence across hundreds of tokens. Standard BERT truncates text at 512 tokens, but Longformer extends this to 4,096 tokens using sparse attention patterns, making it practical for summarizing technical reports. For simpler use cases, averaging word embeddings (e.g., with GloVe) or using Doc2Vec (which learns paragraph-level vectors) can provide a lightweight solution. Another approach is to split documents into chunks, embed each with a model like RoBERTa, and then pool the results. For example, combining max-pooling of chunk embeddings can highlight key themes in a 50-page PDF. While these methods lose some nuance, they balance accuracy and computational cost. Developers should also consider API-based options like OpenAI’s text-embedding-3-large, which handles up to 8,192 tokens natively, though with higher latency.
In practice, the decision hinges on use case constraints. Short text demands speed and semantic precision, favoring smaller models with sentence-level training. Long documents require trade-offs: specialized architectures for full-context accuracy or simpler methods for scalability. Testing with representative data (e.g., comparing SBERT vs. chunked BERT embeddings on your corpus) is often the best way to choose.