# Jointly Extracting and Compressing Documents with Summary State Representations

## TL;DR

This paper introduces a neural model that combines extraction and compression for text summarization, dynamically determining summary length and achieving state-of-the-art results on major datasets.

## Contribution

The model uniquely integrates extraction and compression with dynamic length determination, improving summary conciseness and informativeness without requiring length constraints.

## Key findings

- Achieves state-of-the-art results on CNN/DailyMail and Newsroom datasets.
- Generates concise, informative summaries as confirmed by human evaluation.
- Provides a new dataset of oracle compressive summaries.

## Abstract

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The proposed model offers a balance that sidesteps the difficulties in abstractive methods while generating more concise summaries than extractive methods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstrate that our model generates concise and informative summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMail reference summaries.