Semantic-Summarizer: Semantics-based text summarizer for English language text

Automatic summarization model based on clustering algorithm

“…Although these methods are effective, they mostly did not consider the semantic problem. To alleviate the semantic problem, Mohd et al 7 used a distributional semantic to capture and preserve the semantics of text as the fundamental feature for summarizing; Kirmani et al 8 utilized bio-semantic models on the domain of bio-medical research; Bhat et al 9 used emotion described by text as semantic feature; Kirmani et al 10 proposed an email summarizing system by semantic models and deep-learning technologies to summarize emails; Mud et al 11 proposed an advanced text document summarizer with cluster algorithm to preserving the underlying semantics of the original text. Although the above methods take into account the semantics of preserving the original text, it does not take into account the problem of redundancy, which may make the sentence semantic redundancy in the extracted summary.…”

Biomedical semantic text summarizer

“…Text summarization is helpful as it serves the following purposes: Text summarization produces smaller documents, reducing the input documents’ size and hence a shorter reading time; text summarization helps produce reports used by commercial companies for easier decision-making; text summaries are useful in stock markets and reviewing financial statements; emails are easily comprehensible if email summarization is employed [ 2 ]; for e-learning systems, text summarization is highly beneficial; text summaries are highly useful in determining the polarity and sentiment of a document [ 3 ]. Hence, we find an excellent motivation to work on the text summarization and improve the process [ 4 ].…”