Sentence examples for summarization content from inspiring English sources

Semantic similarity provides meaningful grouping of similar text segments as summarization content units for generating summary of the text collections.

High pyramid score reflects the greater likelihood that more SCUs (Summarization Content Units) in the summary appear in the pyramid [53].

Semantic similarity along with clustering provides a mechanism of participation of the different summarization content units from the different groups of text documents.

It is also clear that clustering along with the semantic similarity provides better summarization content units for generating summary from the text collections.

Case III consider both the textual similarity (using clustering) and semantic similarity which makes sure that best summarization content units participate in the summary generation.

The Pyramid evaluation combines both a precision measure (as the score is a function of the size of the summary) and of a recall measure (as the score is also a function of the weights of the optimal SCUs or Summarization Content Units).

Automatic summarization of content: The goal is to automatically construct summaries from the web page text content [10].

A two-phase process that employs graph summarization and content-based clustering is developed to partition users into different interest groups.

To promote the summarization of contents, however, it is recommended that the New Guidelines present specific considerations for infants, less than three-year olds, and over three-year olds in addition to common features found in all different age groups.

Another approach was presented by Quezada and Poblete [15], which focused on automatic summarization of multimedia content by using social media posts as surrogate text for multimedia documents.

The summarization of discussion content is performed as follows: Based on common visual referents in utterances included in discussion chunks, a graph structure is generated.