Multi-Document Abstractive Summarization using Recursive Neural Network
D Naga Sudha1, Y Madhavee Latha2
1D Naga Sudha*, JNTUH College of Engineering, Jagtial, Telangana, India
2Y Madhavee Latha, Malla Reddy Engineering College for Women, Telangana, India
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 364-370 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5274059720/2020©BEIESP | DOI: 10.35940/ijitee.G5274.059720
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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: Text summarization is an area of research with a goal to provide short text from huge text documents. Extractive text summarization methods have been extensively studied by many researchers. There are various type of multi document ranging from different formats to domains and topic specific. With the application of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. This approach has been attempted for English and Telugu languages in this article. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. The work implements semantic based filtering using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang Similarity and making use of a Recurrent Neural Network (RNN) with an attention mechanism to generate summary. ROUGE score is used for measuring the performance of the applied method on Telugu and English langauges.
Keywords: Abstractive Text Summarization, Multi document, Text Generation, Semantic Role Labeling, Semantic Similarity Matrix, Semantic Selection, ROUGE, Summary Generation.
Scope of the Article: Semantic Web