Assessing books’ depth and breadth via multi-level mining on tables of contents

Compared with journal articles, books can provide broader, deeper and more comprehensive information, and often have higher expertise and academic depth. However, most researches on book assessment focus on measuring academic value of books (e.g. citations analysis) or identifying attitudes of readers (e.g. book review mining), depth and breadth reflected by book contents is neglected. Therefore, in this paper, we measure books’ depth and breadth by mining books’ tables of contents, so as to enrich resources and methods for book assessment research, help users understand book contents quickly and improve efficiency of book selection. Specifically, we measured books’ depth and breadth based on books’ tables of contents via two levels: topic level and feature level. Firstly, we obtained topic-level metrics by identifying topics expressed in tables of contents and calculating topic distributions. Then, we got feature-level results via feature extraction and feature distribution calculation. Finally, we compared depth and breadth metrics and other book assessment metrics. Experimental results reveal that, books’ depth and breadth at two levels are different, and substantial differences between disciplines and book types are obvious. In addition, books’ depth and breadth can provide alternative and supplementary information for assessing multi-dimensional values of books.