Search

This paper outlines methods for text summarizing on the text file and voice audio file conversion. In the first scenario, here converted the audio files to text format using Python tools. Text summarization is done for the latter scenario using modules from Natural Language Processing. The English data functions employ a Python framework called SpaCy. Important sentences discovered during the investigation of the extraction are used in the summarization approach. The number of times a word appears in the text file determines how much weight is given to that word. Using the main audio recording as a starting point, this method is used to create summaries. [ABSTRACT FROM AUTHOR]

The present expansion of unstructured, text-based data in the digital environment necessitates the development of automatic text summary systems that allow users to easily extract insights from them. We now have instant access to massive amounts of information. However, most of this data is superfluous, inconsequential, and may not convey the intended message. To locate the information needed from an online news piece, the user may have to sift through the content and spend a significant amount of time removing extraneous stuff. As a result, using computerized text summarizers that can extract vital information while removing inessential and unnecessary material is becoming increasingly crucial. Implementing summarizing can make documents simpler to read and reduce the amount of time spent searching for information and enable you to cram more details into a smaller area. In general, NLP uses two approaches to summarize texts: extraction and abstraction. ROUGE stands for Recall Oriented Understanding for Gist Evaluation. In this study, ROUGE measurements were used for the summary procedure. In this text summary regressive transformers with a bi-directional auto-encoder, BART has been used as the model. It is a de-noising auto-encoder for seq-to-seq model pre-training. A bidirectional (like BERT) encoder and an autoregressive (like GPT) decoder are combined in the transformer encoder-decoder (seq2seq) paradigm known as BART. The recommended model produced an output summary with a quality score of about 78 percent for a given input text. [ABSTRACT FROM AUTHOR]

Bidirectional Encoder Representations from Transformers (BERT) is one method technique in machine learning for solving various natural language processing (NLP) problems, including summarization. One general problem using BERT is getting much time and resources to train the model. This paper aims to implement the small architecture BERT, called Small2BERT, which is expected to summarize the data. Therefore, this study compares pre-trained Small2BERT and Summarization using Word Frequency (SWF) in extracting text summaries. This research uses the dataset of Indian News Summary, which includes news articles from the Hindu, Indian Times, and Guardian from February to August 2017. Small2BERT surpasses SWF in precision, whereas SWF gives a higher score in overall high f1-score. Small2BERT earns a 0.307 f1-score in Rogue-1, compared to 0.35 for SWF. Small2BERT scores 0.19 and 0.304 while SWF achieves 0.304 and 0.31 in f1-score for Rogue-2 and Rogue-L, respectively. [ABSTRACT FROM AUTHOR]