TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Abstractive Text Summarization	CNN / Daily Mail	EditNet	ROUGE-1	41.42	# 34
Abstractive Text Summarization	CNN / Daily Mail	EditNet	ROUGE-2	19.03	# 31
Abstractive Text Summarization	CNN / Daily Mail	EditNet	ROUGE-L	38.36	# 32

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An Editorial Network for Enhanced Document Summarization

WS 2019 · Edward Moroshko, Guy Feigenblat, Haggai Roitman, David Konopnicki ·

We suggest a new idea of Editorial Network - a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. Our network tries to imitate the decision process of a human editor during summarization. Within such a process, each extracted sentence may be either kept untouched, rephrased or completely rejected. We further suggest an effective way for training the "editor" based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baseline methods.