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Text Summarization Using Cosine Similarity and Clustering Approach

EasyChair Preprint no. 2927

5 pagesDate: March 11, 2020


The document summarization is becoming essential as lots of information getting generated every day. Instead of going through the entire text document, it is easy to understand the text document fast and easily by a relevant summary. Text summarization is the method of explicitly making a shorter version of one or more text documents. It is a significant method of detecting related material from huge text libraries or from the Internet. It is also essential to extract the information in such a way that the content should be of user’s interest. Text summarization is conducted using two main methods extractive summarization and abstractive summarization. When method select sentences from word document and rank them on basis of their weight to generate summary then that method is called extractive summarization. Abstractive summarization method focuses on main concepts of the document and then expresses those concepts in natural language. Many techniques have been developed for summarization on the basis of these two methods. There are many methods those only work for specific language. Here we discuss various techniques based on abstractive and extractive text summarization methods and shortcomings of different methods.

Keyphrases: Extractive summary, Information Extraction, Text Summarization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sushama Pawar and Sunil Rathod},
  title = {Text Summarization Using Cosine Similarity and Clustering Approach},
  howpublished = {EasyChair Preprint no. 2927},

  year = {EasyChair, 2020}}
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