Text Summarization in the Age of Deep Learning: A comprehensive analysis
Maitreya Moharil , Sachin Balvir, Sameer Tembhurney, Samiksha Anjarkar, Bhushan Dhawale, Sharvari Kamble, Karan Dogra
Natural language Processing, Machine learning, Neural Network, Abstractive and Extractive Method.
Massive volumes of data are accessible online thanks to the digital revolution, but finding accurate and pertinent data is not always simple. Search engines are still able to retrieve considerably more information than the average user can handle or control. To make it easy for someone to understand the meaning without reading the entire paper, it is necessary to offer the information in an abstract manner. This work proposes a generator of a literature review summary for a single document. A term- sentence matrix is constructed from the document. To provide a streamlined version of the original text, our work mostly focuses on analytical details. Sentence position, sentence position in relation to the paragraph, the number of named entities, and the feature matrix of each sentence are examples of characteristics that are utilized to increase accuracy without sacrificing the text's core meaning. The paper primarily examines two primary kinds of text summarizing techniques in depth. They are text summaries that are abstractive and extractive. This data set's static analysis validates the outcome of our experiment.
Article Details
Unique Paper ID: 163308

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 1379 - 1388
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