Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization

Autor:	Roitman, Haggai, Feigenblat, Guy, Konopnicki, David, Cohen, Doron, Boni, Odellia
Rok vydání:	2018
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	We propose Dual-CES -- a novel unsupervised, query-focused, multi-document extractive summarizer. Dual-CES is designed to better handle the tradeoff between saliency and focus in summarization. To this end, Dual-CES employs a two-step dual-cascade optimization approach with saliency-based pseudo-feedback distillation. Overall, Dual-CES significantly outperforms all other state-of-the-art unsupervised alternatives. Dual-CES is even shown to be able to outperform strong supervised summarizers.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.00436 Zobrazit plný text záznamu View this record from Arxiv