Insiya Abdulsalam’s scientific contributions

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Fig.3. Text Summarizer Output Apart from the Automation techniques, human skills can go deep into the summarization topic and relate the sentences in more logical tokens which can be constrained to obtain a summary which is more user convenient. Researches related to this technique are still going on and the new features are taken into account for improving the deepness in relating sentence and obtaining well formed, user convenient, more compact summary. This applies the rule-based extractive summary approach and the current GSM method. Summaries generated by the extractive method are compared with GSM generated summaries. The main methods of system performance assessment are recall, accuracy, and f-measurement. Recall is determined by calculating the proportion of corresponding summary sentences. In summary, accuracy is calculated as the proportion of correct sentences. Applying GSM Summarizer and the Rule Extractive Summarizer, F-measurement determined by weighted harmonics recall mean and accuracy values. Calculated re-call, F-measure and accuracy for fifteen DUC dataset sets of documents. Precision (also referred to as positive predictive value) is the fraction of significant instances among the retrieved instances, while recall (also referred to as sensitivity) is a fraction of the actual instances over the total number of important instances. Therefore, both precision and recall are based on an understanding and a measure of relevance. The equations used to calculate recall and precision were:
Fig. 4. Precision
Rule Based Extractive Summarization a nd Title Generation
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March 2020

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International Journal of Innovative Technology and Exploring Engineering

Insiya Abdulsalam

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Shirien K A

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Text summarying is a process by which the most important information from the source document is precisely found. It stands for the information condensed to a longer text. Text summary is broken down into two approaches: extractive summary and abstractive summary. The proposed method creates an extractive summary of a given text and generate an appropriate title for the generated summary. Extractive summary is generated through sentence selection by using Rule-based concept. Eight different features are considered to rank each sentence according to its importance. Ranking assigns a numerical measure to each sentence. After ranking, sentences that has high rank compared to others will be selected to form the summary. The frequently occurring bi-gram is selected as the title for the summary. The system performs better than existing extractive summarization techniques like Graph-based system and achieved a precision of 0.7

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