The technique might not efficiently incorporate the information given or might leave out some crucial details. The keywords are identified in a given text based on the frequency of their occurrences. However, the techniques generate a summary by identifying the imperative sentences. ![]() Extractive summarization techniques are also computationally more feasible to implement since they require fewer resources, computation power, and time to assess and generate a summary since they are statistically oriented. Extractive summarization techniques can extensively analyze the given text semantically, i.e., on sentences, words, keywords, etc., identified by the algorithm. In addition, a summary generated should succeed in incorporating the essential details and the main ideas of the given text. Summarizing textual information requires understanding and analyzing the linguistic, conceptual, and semantic attributes of the given information. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After reviewing the state-of-the-art literature, it generates good summaries results. These algorithms are chosen based on various factors. ![]() We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. Therefore, summary generation is essential and beneficial in the current scenario. ![]() People could not access, read, or use such a big pile of information for their needs. Manual summarization by experts is an almost impossible and time-consuming activity. For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method.
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