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After this, we discuss some popular application areas like information retrieval, question answering, text summarization and text generation.
Keywords: computational linguistics, natural language processing, computer science, information retrieval, question answering, text summarization
Text summarization is a popular application that allows for reduction of text size without significant loose of the content.
This section is divided into following subsections that correspond to each application of CL: Information Retrieval, Question Answering, Text Summarization, and Text Generation.
A solution for such problem is to extract the important parts of the document which is the task of automatic text summarization.
This paper is about the Automatic Summarization task within two different points of view, focusing on two main goals.
Keywords: natural language processing, automatic summarization, relevance detection, quality-based evaluation
Automatic Summarization (AS) task helps users condense all this information and present it in a brief way, in order to make it easier to process the vast amount of documents related to the same topic that exist these days.
The stage of interpretation is what distinguishes extract-type summarization systems from abstract-type systems.
Another essential part of the Text Summarization (TS) task is how to perform the evaluation of a summary.
British Telecommunications announced today that Internet users can access its revolutionary text summarization utility via the World Wide Web.
Users with browsers that are capable of cut-and-paste functionality can use the summarization utility for any document.