Text & Video Summarization with Search

Abstract

Text summarizing is a NLP activity, which reduces massive text volumes into brief summaries. It falls into two categories: abstractive (rephrasing material) and extractive (selecting text parts). Traditional statistical methods and contemporary deep learning techniques are examples of algorithms. While abstractive approaches use Transformer-style sequence-to-sequence models, extractive methods use graph-based algorithms and sentence rating. Keeping context and coherence present challenges. Evaluation criteria that rate summary quality include BLEU and ROUGE. An extension that condenses video material is called video summarization. It has difficulties with visual representation and comprehension of the material. NLP and computer vision methods, such as frame selection and key event extraction, are included in the solutions. With the increasing amount of textual and visual data available, video summarization advances are becoming essential for effective information extraction and decision making.