Review of Recent Methods on Text Summarization and Dimensionality Reduction | Original Article
Nowadays, the exponential growth of World Wide Web (WWW) leads the significant increase in online resources and hence the huge amount of data generated on Internet. Finding the relevant information from such enormous data is challenging tasks, thus the information retrieval (IR) becomes the more vital for searching the relevant data effectively. The Text Search Engines (TSE) returns the large number of pages which becomes very difficult task for end users to identify the relevant page. This process can be smoothening if documents are proved along with its short summary. The terminologies such as text search and text summarization (TS) are becoming the hot topics since from last decade for the researchers. The appropriate text summarization and dimensionality reduction of summarized text can leads to significant reduction in accessing time for the input requirements. TS the data mining process in which the original document is converted to the short version by fetching the key points from the document. The TS approach can be viewed as the Extractive Summarization and Abstractive Summarization. There are number of methods presented for the TS in literature, this paper presents the study on some recent works for TS. For TS, another approach called dimensionality reduction (DR) used to identify the relevant components from the document and remove the irrelevant text information from it. We present the review of some of the DR methods as well in this paper.