There is a lot of information available in the form of the text documents. To gain this knowledge from the text data, its summary is a convenient way to grasp the information quickly. Text summarization applications can be seen where ever text document is involved irrespective of the field. Due to its applications and ease that it brings, text summarization is a popular topic in the field of Natural language processing. The text summarization is categorized as extractive and abstractive summary. Many researchers have proposed methodologies to improve both extractive and abstractive summaries. This study shed light on the different types of summarization, different approaches that are being developed and used for the text summarization. This study also covers the evaluation types that are used for the evaluation of the summary.
Text Summarization, Tokenization, Stop Words, Extractive Summary, Abstractive Summary.
Semi-automated Tools for Systematic Searches.
Adam GP, Wallace BC, Trikalinos TA
Methods in molecular biology (Clifton, N.J.). 2022; 2345(): 17-40
Effect of Telephone Call and Text Message Reminders on Patient Return to Acupuncture Follow-Up Treatment: A Pilot Randomized Controlled Trial.
Lam CN, Ruth C, Chou CP, Black DS
Medical acupuncture. 2021; 33(3): 226-234
Preliminary Study on the Safety and Efficacy of One-Stop Treatment of Percutaneous LAAO Combined with Coronary Intervention for Higher Risk of Bleeding in Patients with AF Complicated with CHD.
Jiang X, Zeng J, Zhong L, Zhu W, Li J
The heart surgery forum. 2021; 24(3): E474-E478
A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
Zhou G, Zhao H
PLoS genetics. 2021; 17(7): e1009697
Phenomenon of conversion in Turkic languages: the process of transition of words into adverbs
E.B. Saurykov, K.K. Molgazhdarov, A.T. Kembaeva, R.A. Berkenova, K.U. Tamabaeva
Elementary Education Online. 2021; 20(5): 1169-1176