Manuscript Number : IJSRSET12310669
Automated Text Summarization as A Service
Authors(4) :-Ketan Shahapure, Samit Shivadekar, Shivam Vibhute, Milton Halem Recent advancements in technology have enabled the storage of voluminous data. As this data is abundant, there is a need to create summaries that would capture the relevant details of the original source. Since manual summarization is a very taxing process, researchers have been actively trying to automate this process using modern computers that could try to comprehend and generate natural human language. Automated text summarization has been one of the most researched areas in the realm of Natural Language Processing (NLP). Extractive and abstractive summarization are two of the most commonly used techniques for generating summaries. In this study, we present a new methodology that takes the aforementioned summarization techniques into consideration and based on the input, generates a summary that is seemingly better than that generated using a single approach. Further, we have made an attempt to provide this methodology as a service that is deployed on the internet and is remotely accessible from anywhere. This service provided is scalable, fully responsive, and configurable. Next, we also discuss the evaluation process through which we came up with the best model out of many candidate models. Lastly, we conclude by discussing the inferences that we gained out of this study and provide a brief insight into future directions that we could explore.
Ketan Shahapure Machine Learning, Natural Language Processing, Automated Text Summarization, Language Modeling Publication Details
Published in : Volume 11 | Issue 1 | January-February 2024 Article Preview
Department of CSEE, University of Maryland Baltimore County, USA
Samit Shivadekar
Department of CSEE, University of Maryland Baltimore County, USA
Shivam Vibhute
San Jose State University, San Jose, California, United States
Milton Halem
University of Maryland, Baltimore County
Date of Publication : 2024-02-29
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 101-112
Manuscript Number : IJSRSET12310669
Publisher : Technoscience Academy
Journal URL : https://ijsrst.com/IJSRSET12310669
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