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Retrieving information from evolving digital data collection using a user's query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection. Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing (NLP) which can be used for precise retrieval and are considered to be the best option. NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features. The study aims to propose a hybrid approach for query-based extractive text summarization in the research study. Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals. Query-based text summarization of multiple documents using a hybrid approach, combining the K-Means clustering technique with Latent Dirichlet Allocation (LDA) as topic modeling technique produces 0.288, 0.631, and 0.328 for precision, recall, and F-score, respectively. The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach. [ABSTRACT FROM AUTHOR]

In recent years, the evolution of technology has led to an increase in text data obtained from many sources. In the biomedical domain, text information has also evidenced this accelerated growth, and automatic text summarization systems play an essential role in optimizing physicians' time resources and identifying relevant information. In this paper, we present a systematic review in recent research of text summarization for biomedical textual data, focusing mainly on the methods employed, type of input data text, areas of application, and evaluation metrics used to assess systems. The survey was limited to the period between 1st January 2014 and 15th March 2022. The data collected was obtained from WoS, IEEE, and ACM digital libraries, while the search strategies were developed with the help of experts in NLP techniques and previous systematic reviews. The four phases of a systematic review by PRISMA methodology were conducted, and five summarization factors were determined to assess the studies included: Input, Purpose, Output, Method, and Evaluation metric. Results showed that 3.5 % of 801 studies met the inclusion criteria. Moreover, Single-document, Biomedical Literature, Generic, and Extractive summarization proved to be the most common approaches employed, while techniques based on Machine Learning were performed in 16 studies and Rouge (Recall-Oriented Understudy for Gisting Evaluation) was reported as the evaluation metric in 26 studies. This review found that in recent years, more transformer-based methodologies for summarization purposes have been implemented compared to a previous survey. Additionally, there are still some challenges in text summarization in different domains, especially in the biomedical field in terms of demand for further research. [ABSTRACT FROM AUTHOR]