Multi-Documents Extractive Text Summarization using Node Centrality
Anish Mathew Kuriakose1, V. Umadevi2
1Anish Mathew Kuriakose*, Research Scholar, Department of Computer Science, Jairams Arts and Science College Karur affiliated to Bharathidasan University Tiruchirappalli, Tamil Nadu, India.
2Dr. V. Umadevi, Director, Department of Computer Science, Jairams Arts and Science College Karur affiliated to Bharathidasan University Tiruchirappalli, Tamil Nadu, India
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2817-2822 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5970029320/2020©BEIESP | DOI: 10.35940/ijeat.C5970.029320
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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: The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.
Keywords: Extractive text summarization, node centrality, Katz centrality.