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Scaling and Semantically-Enriching Language-Agnostic Summarization
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Author(s): George Giannakopoulos (NCSR Demokritos, Greece & SciFY PNPC, Greece), George Kiomourtzis (SciFY PNPC, Greece & NCSR Demokritos, Greece), Nikiforos Pittaras (NCSR Demokritos, Greece & National and Kapodistrian University of Athens, Greece)and Vangelis Karkaletsis (NCSR Demokritos, Greece)
Copyright: 2020
Pages: 49
Source title:
Trends and Applications of Text Summarization Techniques
Source Author(s)/Editor(s): Alessandro Fiori (Candiolo Cancer Institute – FPO, IRCCS, Italy)
DOI: 10.4018/978-1-5225-9373-7.ch009
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Abstract
This chapter describes the evolution of a real, multi-document, multilingual news summarization methodology and application, named NewSum, the research problems behind it, as well as the steps taken to solve these problems. The system uses the representation of n-gram graphs to perform sentence selection and redundancy removal towards summary generation. In addition, it tackles problems related to topic and subtopic detection (via clustering), demonstrates multi-lingual applicability, and—through recent advances—scalability to big data. Furthermore, recent developments over the algorithm allow it to utilize semantic information to better identify and outline events, so as to offer an overall improvement over the base approach.
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