DB-FSFO – “A Division-Based Feature Selection Flow Optimization Model for Better Summaries and Reading Recommendations
Date
2018-11-02
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Abstract
With 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.
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Automatic Text Summarization, Machine Learning
Citation
Sharma, 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/33251