# An Editorial Network for Enhanced Document Summarization

## TL;DR

This paper introduces an Editorial Network that combines extractive and abstractive summarization as a post-processing step, mimicking human editing to improve document summaries.

## Contribution

It proposes a novel mixed extractive-abstractive approach with a soft-labeling training method, enhancing summarization quality over existing methods.

## Key findings

- Outperforms state-of-the-art extractive and abstractive baselines
- Effective soft-labeling training approach demonstrated
- Improved summary coherence and relevance

## Abstract

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.