Advancements and Challenges in Text Summarization: An Overview of Methods and Strategies in Brief

Information retrieval and natural language processing have both benefited greatly from the use of text summarization techniques. An efficient means of summarizing text has become essential due to its exponential growth. The numerous text summarization strategies that have been created and investigated recently are covered in detail in this paper. The first section of the review looks at extractive summarizing strategies, which involve selecting and incorporating the most important phrases or lines from the source text. The paper then explores abstractive summaries, which aim to create summaries by rewriting and paraphrasing the source material. The capacity of deep learning techniques, specifically, sequence-to-sequence models that incorporate attention mechanisms, to learn contextual information and produce logical and succinct summaries is investigated. The review also emphasizes the rise of hybrid approaches, which incorporate aspects of abstractive and extractive methods. The model and the outcomes it produced for extractive, abstractive, and hybrid approaches were also presented in this work.