An Extractive Summarization Technique for Text Documents
Ashwitha Dantis1, Roshan Fernandes2, Anisha P Rodrigues3
1Ashwitha Dantis, Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India.
2Roshan Fernandes, Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India.
3Anisha P Rodrigues, Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1202-1206 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8369088619/2019©BEIESP | DOI: 10.35940/ijeat.F8369.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In order to read as well as search information quickly, there was a need to reduce the size of the documents without any changes to its content. Therefore, in order to solve this problem, there was a solution to it by introducing a technique called as automatic text summarization which is used to generate summaries from the input document by condensing large sized input documents into smaller documents without losing its meaning as well as relevancy with respect to the original document. Text summarization stands for shortening of text into accurate, meaningful sentences. The paper shows an implementation of summarization of the original document by scoring the sentence based on term frequency and inverse document frequency matrix. The entire record was compressed so that only the relevant sentences in the document were retained. This technique can be applicable in various applications like automating text documents, quicker understanding of documents because of summarization.
Keywords: Term frequency, Inverse document frequency, Sentence ranking, Sentence score, Pos-tagging.