Semantic Based Content Search and Content Summarization
type: CreativeWork
description: Document summarization has been an intriguing task of Computational linguistics. A number of definitions have been proposed in References, all of which consider document summarization as a problem of text compression. One of the most complete definitions by Sparck-Jones states that "...a summary is a reductive transformation of source text to summary text through content condensation by selection and/or generalisation on what is important in the source...". The importance of document summarization does not lie only in presenting information in a shortened form, but also in selecting the most appropriate content to present. Moreover, a main feature in summarization is the number of sources from which a summary may be produced; thus, single-document and multi-document have been proposed, denoting the number of sources from which the summary will be produced. In addition, another categorization that may be extracted from this definition refers to the importance of the source, and what the potential user thinks is important. This leads to the definition of generic and query-based or task focused summarization, where generic implies that the summarizer should extract information according to the main topics discussed in the document, while query-based summarization focuses on extracting information according to simple or more complex questions on the document. Moreover, importance of content can be extracted through knowledge-rich (supervised and semi-supervised summarization) and knowledge lean approaches (unsupervised or shallow summarization). The last categorization refers to the type generation of the summary, the two main categories being: extractive summarization, where sentences are maintained in the summarization process unaltered; and abstraction, where the sentences are either semantically altered or compressed. The research depicted in this thesis, presents novel document summarization approaches based on the theories of Machine Learning (ML) and Natural Lang…
abstract: Document summarization has been an intriguing task of Computational linguistics. A number of definitions have been proposed in References, all of which consider document summarization as a problem of text compression. One of the most complete definitions by Sparck-Jones states that "...a summary is a reductive transformation of source text to summary text through content condensation by selection and/or generalisation on what is important in the source...". The importance of document summarization does not lie only in presenting information in a shortened form, but also in selecting the most appropriate content to present. Moreover, a main feature in summarization is the number of sources from which a summary may be produced; thus, single-document and multi-document have been proposed, denoting the number of sources from which the summary will be produced. In addition, another categorization that may be extracted from this definition refers to the importance of the source, and what the potential user thinks is important. This leads to the definition of generic and query-based or task focused summarization, where generic implies that the summarizer should extract information according to the main topics discussed in the document, while query-based summarization focuses on extracting information according to simple or more complex questions on the document. Moreover, importance of content can be extracted through knowledge-rich (supervised and semi-supervised summarization) and knowledge lean approaches (unsupervised or shallow summarization). The last categorization refers to the type generation of the summary, the two main categories being: extractive summarization, where sentences are maintained in the summarization process unaltered; and abstraction, where the sentences are either semantically altered or compressed. The research depicted in this thesis, presents novel document summarization approaches based on the theories of Machine Learning (ML) and Natural Lang…
author: Mamakis , Georgios
language: eng
keywords: Computational linguistics
date published: 2012-10
date modified: 2026-07-24T04:39:31Z
provider: University of South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of South Wales