Automatic Summarization of Textual Document
Faiyaz Ahmad1, Yassar2, Amreen Ahmad3
1Faiyaz Ahmad*, Dept. of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
2Yassa, Dept. of Computer Engineering, Jamia Millia Islamia , New Delhi India.
3Amreen Ahmad, Dept. of Computer Engineering, Jamia Millia Islamia , New Delhi, India.
Manuscript received on October 11, 2019. | Revised Manuscript received on 23 October, 2019. | Manuscript published on November 10, 2019. | PP: 2486-2491 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4400119119/2019©BEIESP | DOI: 10.35940/ijitee.A4400.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today world, there is a huge amount of information is growing every day on the internet and from many other sources and there is lots of textual information in it. To find out the relevant information from this large amount of data, we need an automatic mechanism that will extract the useful data. Such automatic systems are automatic summarization systems. They categorized into extractive and abstractive summarization system. Extractive summarization systems select the important sentences directly from the large document and put into summary whereas abstractive methods understand semantic meaning of the document by linguistic method to interpret and examine the text. In the purposed method, a statistical approach is used where multiple criterions or features are discussed to calculate the score for every sentence and then SIR (Susceptible Infected Recovered) model is used to compute the dynamic weight for every feature. After dynamic weight computation, weighted TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is used for multi-criterion analysis and aggregation. This method is fully implemented and integrated for automated textual document summarization system.
Keywords: Extractive, SIR, Weighted TOPSIS, Single Document, Multi-Document, Generic Summaries, Precision and Recall, Bigrams and skip-gram
Scope of the Article: Bioinformatics