A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Training a selection function for extraction
Proceedings of the eighth international conference on Information and knowledge management
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
A statistical approach to mechanized encoding and searching of literary information
IBM Journal of Research and Development
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It is important for researchers/investigators to read and understand scientific papers effectually and effectively. However, it takes much time and many efforts to read and understand many papers related directly to their researches, even if they could refer to necessary papers timely. In this paper, we address a function for supporting the scientific paper understanding process successfully. We focus on figures which can usually explain the important topics along a series of successive paragraphs, and develop an intellectual tool which collects the mutually related sentences, attended interdependently to the focused figure, and supports a paper understanding ability through the focused figure. In this paper, we introduce the propagation mechanism of important words over the corresponding sentences. This propagation mechanism can select candidate sentences appropriate to explain the focused figure.