Sentence examples for a large text document from inspiring English sources

Document processing and summary generation in a large text document collection is computationally complex task and in the era of Big Data analytics where size of data collections is high there is need of algorithms for summarizing the large text collections rapidly.

In single-document summarizers, a single large text document is summarized to another single document summary, whereas in multi-document summarization, a set of text documents (multi documents) are summarized to a single document summary which represents the overall glimpse of the multiple documents.

For example, Cambiera [16] is a visual analytics solution which supports collaborative searching through large text document collections on a touch surface.

Cambiera (Isenberg and Fisher 2009), a tabletop visual analytics tool, supports information foraging activities in large text document collections.

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

It greatly increases the value of such powerful information systems as Scopus®, SciFinder®, Reaxys® which are capable of handling large text document databases and especially those fitted with advanced text information retrieval capabilities.

A summarized document helps in understanding the gist of the large text collections quickly and also save a lot of time by avoiding reading of each individual document in a large text collection.

Pick out a large-text book.

In this paper, we show how the required semantic relations can be learned from a large collection of text documents.

Use an electronic text document (such as a Microsoft Word file) or a sheet of lined paper.

Each verbatim transcription then served as a primary text document.