Summades

Summades are compact data units derived from large text corpora, multimedia collections, or sensor streams by applying summarization techniques that preserve core meaning and context. A summade typically combines a short textual abstract with structured metadata and a semantic embedding, enabling efficient indexing and retrieval.

Origin and use: The term is used in experimental settings in information retrieval and natural language processing to describe an end product of hybrid summarization pipelines that merge extractive and abstractive approaches. Summades can be created automatically from primary sources and stored in archives or knowledge graphs.

Composition and formats: A summade includes a content digest, a rationale or highlights section, a list of source references, a confidence score, and vector embeddings representing semantics. Formats vary by system but commonly use JSON or a compact binary form.

Applications and benefits: Summades are used to accelerate search, facilitate rapid comprehension, train compact AI models, and aid archival access. They reduce storage and bandwidth needs while maintaining essential information. Limitations include potential loss of nuanced details and the risk of misinterpretation if the summarization is biased or incorrect.

Related topics include summarization, embeddings, data compression, and knowledge graphs.