An improved method of automatic text summarization for web contents using lexical chain with semantic-related terms
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Many researches have been converging on automatic text summarization as increasing of text documents due to the expansion of information diffusion constantly. The objective of this proposal is to achieve the most reliable and substantial context or most relevant brief summary of the text in extractive manner. The extractive text summarization produces the short summary of a certain text which contains the most important information of original text by extracting the set of sentences from the original document. This paper proposes an improved extractive text summarization method for documents by enhancing the conventional lexical chain method to produce better relevant information of the text using three distinct features or characteristics of keyword in a text. The keyword of the document is labeled using our previous work, transition probability distribution generator model which can learn the characteristics of the keyword in a document, and generates their probability distribution upon each feature.
KeywordsAutomatic text summarization Keyword extraction Lexical chain Markov chain WordNet Semantic-related terms Web contents Machine learning
This study was supported by research Fund from Chosun University, 2015.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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