DB-FSFO – “A Division-Based Feature Selection Flow Optimization Model for Better Summaries and Reading Recommendations

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dc.contributor.advisorRokne, Jon G.
dc.contributor.authorSharma, Sahil
dc.contributor.committeememberAlhajj, Reda
dc.contributor.committeememberKawash, Jalal
dc.date2019-06
dc.date.accessioned2018-11-05T18:53:53Z
dc.date.available2018-11-05T18:53:53Z
dc.date.issued2018-11-02
dc.description.abstractWith constant improvements in digital media technology, there has been a big growth in the quantity of research material available on-line for a researcher to supplement his work. An average researcher typically spends hours to study a research paper, trying to understand all its details and complexities. Sometimes this time spent is not quite justified since the paper might not be highly relevant to the reader’s research. Furthermore, this one paper might just be a small subset of the available information which a researcher would need. A researcher only has limited time and resources to deal with the challenge of accomplishing their reading goals. One approach to alleviate this is by shortening the size of a research document, thereby effectively reducing the time spent by a researcher on reading. Therefore, this thesis aims to provide a system that tackles the complex task of research text summarization. The model, DB-FSFO (Division Based – Feature Selection based Flow Optimization), makes use of Natural Language Processing tools combined with extensive Feature Extraction and Selection procedures to self-weigh the importance of various parameters of a research text document with the corpus in perspective. The final summary produced by the model is the result of a flow optimization through a Reinforcement Learning approach with an extended post-processing accuracy improvement. The model proposed in the thesis is also tested for robustness and versatility by effectively producing recommendations for the next papers to be read by the researcher. This is supplemented further by generation of a reading recommendation graph. Therefore, the DB-FSFO model makes absorbing the essentials of a research paper easier and more efficient.en_US
dc.identifier.citationSharma, S. (2018). DB-FSFO – “A Division-Based Feature Selection Flow Optimization Model for Better Summaries and Reading Recommendations (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33251en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/33251
dc.identifier.urihttp://hdl.handle.net/1880/108956
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectAutomatic Text Summarization
dc.subjectMachine Learning
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationComputer Scienceen_US
dc.titleDB-FSFO – “A Division-Based Feature Selection Flow Optimization Model for Better Summaries and Reading Recommendations
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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