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@inproceedings{1489716, author = {Gallo, Matej and Popelínský, Lubomír and Vaculík, Karel}, address = {Košice}, booktitle = {ITAT 2018 Proceedings,}, editor = {S. Krajči}, keywords = {text summarization; dynamic graph mining}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Košice}, isbn = {978-1-72726-719-8}, pages = {28-34}, publisher = {Safarik University, Faculty of Science, Kosice, Slovakia}, title = {To text summarization by dynamic graph mining}, year = {2018} }
TY - JOUR ID - 1489716 AU - Gallo, Matej - Popelínský, Lubomír - Vaculík, Karel PY - 2018 TI - To text summarization by dynamic graph mining PB - Safarik University, Faculty of Science, Kosice, Slovakia CY - Košice SN - 9781727267198 KW - text summarization KW - dynamic graph mining N2 - 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. ER -
GALLO, Matej, Lubomír POPELÍNSKÝ and Karel VACULÍK. To text summarization by dynamic graph mining. Online. In S. Krajči. \textit{ITAT 2018 Proceedings,}. Košice: Safarik University, Faculty of Science, Kosice, Slovakia, 2018, p.~28-34. ISBN~978-1-72726-719-8.
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