Extractive text summarization for scientific journal articles using long short-term memory and gated recurrent units

Abstract

Along with the increasing number of scientific publications, many scientific communities must read the entire text to get the essence of information from a journal article. This will be quite inconvenient if the scientific journal article is quite long and there are more than one journals. Motivated by this problem, encourages the need for a method of text summarization that can automatically, concisely, and accurately summarize a scientific article document. The purpose of this research is to create an extractive text summarization by doing feature engineering to extract the semantic information from the original text. Comparing the long short-term memory algorithm and gated recurrent units and were used to get the most relevant sentences to be served as a summary. The results showed that both algorithms yielded relatively similar accuracy results, with gated recurrent units at 98.40% and long short-term memory at 98.68%. The evaluation method with matrix recall-oriented understudy for gisting evaluation (ROUGE) is used to evaluate the summary results. The summary results produced by the LSTM model compared to the summary results using the latent semantic analysis (LSA) method were then obtained recall values at ROUGE-1, ROUGE-2, and ROUGE-L respectively were 76.25%, 59.49%, and 72.72%.

Text classification to predict skin concerns over skincare using bidirectional mechanism in long short-term memory

“…When compared with its predecessor vanilla RNN algorithm which is unable to use past information, LSTM outperforms it with its long-term memory. LSTM transforms the memory shape of cells withinside the RNN via way of means of reworking the tanh activation characteristic layer withinside the RNN right into a shape containing memory devices and gate mechanisms, pursuits to determine how to make use of and replace data saved in memory cells [21]. Now, there is a new concept of mechanism in those sequence feed-forward neural network which called bidirectional.…”