Detection of Words Accepted to Dynamic Abstracts Focusing on Local Variation of Word Frequency

Through the widely-spread of digital devices such as smartphone, the digital books have become more popular. We are aiming to develop a system to support reading novel taking an advantage of digital books. In this paper, we propose an elemental method to generate dynamic abstracts for each reading progress. Generating dynamic abstract can be assumed as a topic of summarization task in the field of natural language processing. The proposed method focuses on the local variation of word importance, though some existing criterions for summarization focus on the overall word importance. We prepared four types of local variation and compared the effectiveness of those with each other. We conducted the experiment to detect words accepted to manually-generated dynamic abstracts with each types of the proposed method while the general word importance criterion ( tf-idf ) is used as the comparative method. Through the discussions of the results, it was confirmed that some types of the proposed method were more effective to detect the words accepted to dynamic abstracts than the comparative method.