Automated Text Summarization as A Service

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.