Sentence examples for summarization are from inspiring English sources

The task demands of summarization are closely related to the characteristics of source texts, and genre is an essential characteristic.

Martinez and Martinez [30] emphasize that machine translation, part-of-speech tagging, word sense disambiguation, and text summarization are some of the identified applications that statisticians can contribute.

In this work the problems with multi-document text summarization are addressed with the help of latest technologies in text analytics.

Some examples of the applications of the Multi-document summarization are analyzing the web search results for assisting users in further browsing [1], and generating summaries for news articles [2].

These steps involving background correction, normalization, and summarization are often combined into a single all-in-one preprocessing algorithm that takes raw probe intensities as input and produces gene expression estimates as output.

For the purposes of this paper, quantile normalization and median polish summarization are used for DFCM.

The algorithm performs the task of text summarization is called as text summarizer.

Multi-document summarization is a technique used to summarize multiple text documents and is used for understanding large text document collections.

Text summarization is either extractive or abstractive.

The idea of data summarization is simple.

Fractal summarization is developed based on the fractal theory.