Importance Of Automatic Summarization

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Abstract— Automatic summarization has been an area of interest in the field of natural language processing. Automatic text summarization is the process of producing a summary from a given document automatically. The proposed method is a sentence extraction based single document text summarization which produces a generic summary for a given Malayalam document (Extractive summarization). Sentences in the document are ranked based on the word score of each word present in it. Top N ranked sentences are extracted and arrange them in their chronological order for summary generation. Where N determines the size of summary with respect to the percentage of original document size (condensation rate). Performance evaluation is done by using the standard…show more content…
Some of them duplicates the content present in others. Whenever a user needs to get some information, he needs to retrieve these documents and read them completely to understand the content. This is a time consuming and tedious process and is very difficult for human beings to manually summarize these large documents of text. There comes the importance of automatic summarization. Text summarization is the condensation of the source text by reducing the size along with preserving significant information content and overall meaning. The text summarization done with the machine is termed as automatic text summarization. Automated summarization tools can help people to grasp the main information contents in a short time. A good summary can help the reader to get a quick overview of an entire document. Mainly the text summarization task can be broadly classifieds into two groups: extractive summarization and abstractive summarization. In extractive summarization, important and meaningful sentences or phrases from the source documents are extracted. These sentences are arranged chronologically to produce the summary. In extractive summary, there is no word or phrase present in the…show more content…
Early works in summarization started with single document summarization. In single document summarization, it produces summary of a single document supplied by the user. As research proceeded, and due to large amount of information on web, multi document summarization emerged. Because information is spread over different documents, it need to collect from these different documents. In multi document summarization, it produces summaries from many source documents on the same topic or same event. Multi-document summarization is obviously a more complex task than the single-document summarization. There are two major reasons for this. Firstly, information overlap between the documents can lead to redundancy in the summary. Secondly, an extra effort is required to organize the information from several documents to a coherent summary. The automatic summarization of text is a well-known task in the field of natural language processing (NLP). Significant achievements in text summarization have been obtained using sentence extraction and statistical analysis. Malayalam is a language spoken in India, predominantly

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