A Review on Text Summarization Techniques

Abstract

In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful.We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges. Index Terms: Summarization, Graph based summarization, Meta-heuristic based summarization, Maximal marginal relevance based summarization, Evaluation

SASHAKT: A Job Portal for Women using Text Extraction and Text Summarization

“…These techniques can extract information from job postings and summarize it concisely, making it easier for job seekers to identify job opportunities that align with their skills and qualifications. Common text extraction and summarization techniques include keyword extraction, named entity recognition, and sentence compression [14], [15], [16]. Text classification algorithms are commonly used in natural language processing (NLP) to classify text into different categories or labels.…”