English

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

Computation and Language 2018-11-02 v1

Abstract

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.

Keywords

Cite

@article{arxiv.1811.00436,
  title  = {Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization},
  author = {Haggai Roitman and Guy Feigenblat and David Konopnicki and Doron Cohen and Odellia Boni},
  journal= {arXiv preprint arXiv:1811.00436},
  year   = {2018}
}
R2 v1 2026-06-23T05:00:49.489Z