Summarization in the text analytics industry refers to the process of generating a concise and/or representative summary of a larger document or set of documents. This is often done using algorithms that identify and extract key sentences or paragraphs from the original text, though other methods (including manual extraction) may be used as well.

Outside of this industry, summarization refers more generally to any process of creating a shortened or condensed version of something. For example, a student might produce a summary of a chapter from a textbook, or a business owner might create an executive summary of a report. In these cases, the process of summarization may be more manual, with the individual selecting which information to include and how to present it concisely. However, the goal is typically the same: to create a version of the original that is smaller and easier to digest.

Summarization vs Abstraction vs Condensation

There is some overlap between the concepts of summarization and abstraction, though generally speaking, abstraction takes a more general approach and may involve rephrasing or omitting details that are not deemed essential. Similarly, condensation is another related term that refers to the creation of a shorter version of a text, though this often includes leaving out information that is considered non-essential. In contrast, summarization typically aims to retain as much information from the original text as possible while still keeping the summary concise.

Two Types of Summarization

Dynamic summarization algorithms create a summary by selecting the sentences that best represent the document as a whole. These algorithms often use techniques such as latent semantic analysis (LSA) or Latent Dirichlet allocation (LDA) to find the most important sentences in the document. Static summarization algorithms, on the other hand, generate summaries without taking into account the order of the sentences in the document. These algorithms typically extract key phrases or sentences from the text using rule-based methods.

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