Abstract
In today’s era, information has been growing exponentially on the web, due to which extraction of relevant and concise information has become a challenging task. To overcome the above problem, a fundamental tool known as summarization techniques has been used for understanding and organizing such large datasets. Recently, researchers have been devoting a lot of effort to develop semantics-based models, so as to improve summarization performance. In this paper, a versatile and principled game theory-based multi-document summarization framework integrated with Wikipedia ontology is proposed. The framework exploits the submodularity hidden in underlying ontology and is optimized using the proposed improved algorithm, to enhance the summarization performance. Results of the proposed approach were evaluated with the ROUGE evaluation metric for different multi-document summarization tasks against human-generated summaries and it outperformed DUC, TAC competitors, and other competitive methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.