Intelligent Content Conciser using Pointer Generated Network
Kalpana Devi S1, Nithya R2, Oviyaa B3, Sandhya G4
1Kalpana Devi S, Assistant Professor, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu, India.
2Nithya R, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu, India.
3Oviyaa B, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu, India.
4Sandhya G, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11810-11814 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9198118419/2019©BEIESP | DOI: 10.35940/ijrte.D9198.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The combination of best suited architecture and successful algorithm results in the increased nature of efficient learning among the end users. To increase the number of quality learner’s text summarization provides the best initiative among the readers and learners. As words and sentences comprise a document, document summarization finds diverse words with different sets of synonyms by performing training activity for the process. The S2S(Sequence to Sequence) training mechanism describes the embedding way of sentences and documents. The pointer generation enhances the new hybrid model for summary extraction. The proposed model implements attention mechanism and uses Recurrent Neural Network with LSTM cells at encoder and decoder. The working model focuses on many factors for summary extraction such as sentence/document similarity, repeatedness, indexing and sentence-context richness. It also keeps track of summarized text using coverage to avoid repetition.
Keywords: Abstractive Summarization ,Hybrid Model ,LSTM Cells , Neural Network, Pointer Generator, Recurrent Neural Network , S2S(Sequence To Sequence).
Scope of the Article: Sensor Networks, Actuators for Internet of Things.