A TAXONOMY FOR TEXT SUMMARIZATION

OTHMAN B.M.M.¹*, HAGGAG M.², BELAL M.^3
1Institute of Statistical Studies and Research, Cairo University, Giza, Egypt.
²Department of Computer Sciences, Faculty of Computer Sciences and Information Systems, Helwan University, Cairo, Egypt.
³Department of Computer Sciences, Faculty of Computer Sciences and Information Systems, Helwan University, Cairo, Egypt.
* Corresponding Author : b.m.m.othman@gmail.com

Received : 03-11-2014     Accepted : 20-03-2014     Published : 31-03-2014
Volume : 3     Issue : 1       Pages : 43 - 50
Inform Sci Tech 3.1 (2014):43-50

Keywords : Text summarization, survey, taxonomy
Conflict of Interest : None declared

Text summarization is the branch of NLP where a computer summarizes a text. A text is entered into the computer, a specific technique is applied, and then a summarized text is returned. This summary should be a non-redundant extract from the original text. There are many categories for summarization: single document, multi-document, extractive, abstractive, informative, indicative, user- focused, generic, statistical, linguistic, and machine learning approach based. However, most of the surveys that concerned with text summarization was covering a specific perspective of the field and didn’t clearly illustrate the whole picture of the state- of- the art they covered; the purpose of this survey is to clearly illustrate the whole picture of the previous work in the field of text summarization introducing a general taxonomy that covers all possible aspects of categorizing the text summarization field with clear comparison based on all aspects and features that the text summarization field could have. In this paper all approaches for single document and multi-document summarization, extractive and abstractive summary construction methods, and informative and indicative information content will be introduced. Additionally, query-based and generic summary triggers, statistical, linguistic and machine learning methods for choosing the most relevant sentences from documents had been explored. All these approaches will be introduced and discussed.

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