University of Bahrain
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A Statistical and Machine Learning Approach for Summarising Computer Science Research Papers

Show simple item record Bauboorally, Sheik Muhammad Wakeel Pudaruth, Sameerchand 2023-03-02T12:25:25Z 2023-03-02T12:25:25Z 2023-03-02
dc.identifier.issn 2210-142X
dc.description.abstract Academics, researchers and students usually read a lot of papers for their research or to keep up-to-date with the latest works. The high number of papers available makes the process time-consuming. A solution is to summarise the papers and allow the reader to decide if the papers are relevant to their work and whether they require more attention. A system has been built to generate extractive summaries of computer science research papers. We demonstrate how the intrinsic statistical characteristics of computer science research papers such as the document length or the presence of certain keywords can help train a machine learning classifier model that can achieve state-of-the-art performance. Human and automatic evaluation using ROUGE has been carried out to measure performance. Results show that the proposed model performs better than TextRank and BERT on both human and automatic evaluation. It also does better than BART on human evaluation. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject research paper, computer science, extractive summaries, statistical, machine learning en_US
dc.title A Statistical and Machine Learning Approach for Summarising Computer Science Research Papers en_US
dc.type Article en_US
dc.identifier.doi en
dc.contributor.authoraffiliation Department of Information and Communication Technologies, University of Mauritius, Mauritius en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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