Abstract
As the number of textbooks soars, people may be stuck into thousands of books when learning knowledge. In order to provide a concise yet comprehensive picture for learning, we propose a novel framework, called MM4Books, to automatically build metro maps for efficient knowledge learning by summarizing massive electronic textbooks. We represent each book in digital libraries as a sequence of chapters, and then obtain learning objects by clustering the semantically similar chapters via an unsupervised clustering method to create a learning graph, and then build the metro map by applying an integer linear programming-based technique to select a collection of high informative and fluent but low redundant learning paths from the learning graph. To the best of our knowledge, it is the first work to address this task. Experiments show that our proposed approach outperforms all the state-of-the-art baseline approaches, and we also implemented a practical MM4Books system to prove that users can really benefit from the proposed approach for knowledge learning.
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Acknowledgements
This work is supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY17F020015), the Chinese Knowledge Center of Engineering Science and Technology (CKCEST), the Fundamental Research Funds for the Central Universities (No. 2017FZA5016), and MOE-Engineering Research Center of Digital Library.
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Lu, W., Ma, P., Yu, J. et al. Metro maps for efficient knowledge learning by summarizing massive electronic textbooks. IJDAR 22, 99–111 (2019). https://doi.org/10.1007/s10032-019-00319-y
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DOI: https://doi.org/10.1007/s10032-019-00319-y