Exploring Recent Innovations in Text Summarization: Constructing a Sequence-to-Sequence Model for Enhanced Summarization

The practice of condensing a larger text into a summarized version while keeping the major ideas and important details is known as text summarizing. Either a human being can accomplish this manually or computer algorithms can do it automatically. Text summary techniques have been developed by researchers since the 1950s. However, more recent summarizing initiatives also produced summaries from collections of publications. Most summarizing involves summaries of a single document. This paper assesses contemporary ways to summarize text in following categories: extractive, abstractive, and hybrid text summarizing. It also discusses specific strategies within each category. Furthermore, it discusses about implementation of text summarizer developed using sequence-to-sequence model and Long short-term memory (LSTM) model.