Dynamic summarization is the process of creating a summary of a text that can change based on different criteria. This is in contrast to static summaries, which are fixed and do not change. Dynamic summizations can be based on different factors such as the length of the text, the topic, or the audience.
Summarization is a task in natural language processing where the goal is to create a shorter version of a text that retains the most important information. Summarizations can be done manually or automatically. Automatic summarization algorithms have been developed that can create summaries without human intervention.
There are many applications for automatic summarization, such as reducing the amount of time needed to read a document, extracting key information from a document, or creating summaries of large documents that can be browsed quickly.
Dynamic summarization is commonly used in the text analytics industry to create summaries of texts that can be customized for different audiences or purposes. For example, a dynamic summarization algorithm could be used to create a summary of an article that is targeted at readers who are interested in the main points of the article, and a different summary of the same article that is targeted at readers who are interested in all the details.
Dynamic summarization can also be used outside of the text analytics industry. For example, it can be used to create summaries of meeting minutes that can be customized for different members of the meeting. It can also be used to create summaries of customer feedback that can be customized for different departments within a company.
Dynamic summarization is similar to other tasks in natural language processing such as text classification and information extraction. However, there are some important differences. First, dynamic summarization algorithms usually do not require training data, while algorithms for other tasks such as text classification and information extraction often do. Second, the output of a dynamic summarization algorithm is typically a summary of the input text, while the output of an algorithm for another task such as text classification or information extraction is typically not a summary of the input text.