Automatic Text Summarization
Vishal Tyagi, Utkarsh Dwivedi, Yash Kumar Sharma, Tarun Agarwal
AI (Artificial Intelligence), DL (Deep Learning), DRM (Device Rights Management), HTML (Hyper Text Markup Language), HTTP (Hyper Text Transfer Protocol), NLP (Natural Language Processing), NLTK (Natural Language Tool Kit), XML (Extensible Markup Language)
The objective of this briefing is to propose an application that simplifies the process of extracting meaning from large documents and websites, by making the use of text summarization, i.e., extracting only meaningful excerpts from a large document or website. The Internet is amassed with text data that spans hundreds of pages, sometimes even greater, that requires its meaning to be extracted to both better help discover relevant information and to consume relevant information faster. This briefing proposes an application for the same, in two paradigms i.e., a web-based application, and a Chrome extension. This is an AI based application, using state-of-the-art technologies such as NLP. Two algorithms are proposed for the solution, Abstractive and Extractive Text Summarization; and their results are compared for a sample web page and a document, along with a user-specified web page and document respectively. Along with a dedicated backend occupied by NLP model, a middleware would also be developed as a part of the application, whose sole purpose is the preprocessing of web page as well as documents. At the end of the briefing, the deployment strategies of the application would be discussed, along with the limitations encountered while designing the application. The briefing would be concluded with the future works that are needed with the application, along with the references used in the development of this briefing.
Article Details
Unique Paper ID: 151847

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 982 - 988
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