Abstract
The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.
Highlights
The extensive use of Internet has caused a vast growth in the usage of digital information
Our Approach This paper presents a combined approach by using topic queries or important keywords corresponding to the document set and the fundamental concept of clustering as well as language features to extract the relevant sentences from the original document set
IMPACT OF CLUSTERING IN AUTO TEXT SUMMARIZATION The concept of clustering is very helpful in the text domain as document objects as words, sentences, paragraphs to be clustered are of varying granularities
Summary
The extensive use of Internet has caused a vast growth in the usage of digital information. There are two types of text summarization techniques, generic and query specific [4] It becomes a difficult task for the user to go through a large number of retrieved documents [5]. This difficulty can be resolved with the use of query specific document summary generation. Feature Based Method The extractive type summarization approach identifies the most related sentences from the original text and place them together to generate a concise summary. E. Our Approach This paper presents a combined approach by using topic queries or important keywords corresponding to the document set and the fundamental concept of clustering as well as language features to extract the relevant sentences from the original document set.
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