To text summarization by dynamic graph mining

This publication doesn't include Faculty of Arts. It includes Faculty of Informatics. Official publication website can be found on muni.cz.

Authors

GALLO Matej POPELÍNSKÝ Lubomír VACULÍK Karel

Type Article in Proceedings
Conference ITAT 2018 Proceedings,
MU Faculty or unit

Faculty of Informatics

Citation
Keywords text summarization; dynamic graph mining
Description We show that frequent patterns can contribute to the quality of text summarization. Here we focus on single-document extractive summarization in English. Performance of the frequent patterns based model obtained with DGRMiner yields the most relevant sentences of all compared methods. Two out of three proposed methods outperform other methods if compared on ROUGE data.
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