Text Summarizer with Sequence-To-Sequence Model - Reddy, Veluru Karthik; Prashanth, Vanapalli Durga; Krishna, R. Shiva Rama; lekha, Naidu Sri; N. M. , Jyothi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The utilization of a Sequence-to-Sequence model in the realm of automated text summarization has evolved into an indispensable instrument for the effective handling of prodigious volumes of textual data.This method uses a decoder to produce a summary of the content after processing the input text through an encoder. The attention method, which aids the model in prioritizing essential information during summary creation, is the main innovation. To teach the model how to produce effective summaries, it must be exposed to pairs of source texts and their matching target summaries. This model is useful for a variety of natural language processing applications since it can efficiently summarize new, unread content when used in the actual world. In addition, the Sequence-to-Sequence model has proven effective in several applications, such as content extraction, document summarization, and news item summarization. By drawing crucial insights from large amounts of textual data, this technology responds to the growing problem of information overload by enabling more effective information retrieval and decision-making procedures.