# A novel repetition normalized adversarial reward for headline generation

## TL;DR

This paper introduces a new repetition normalized adversarial reward mechanism that enhances headline generation by reducing repetition and incoherence, leading to significant improvements over baseline models.

## Contribution

It proposes a novel reward function combining repetition normalization and adversarial training to improve language generation quality.

## Key findings

- Reduces repetition rate by 4.98%.
- Improves ROUGE-1 score by 3.24 points.
- Enhances coherence in generated headlines.

## Abstract

While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases \cite{paulus2017deep}. In this paper, we propose a novel repetition normalized adversarial reward to mitigate these problems. Our repetition penalized reward can greatly reduce the repetition rate and adversarial training mitigates generating incoherent phrases. Our model significantly outperforms the baseline model on ROUGE-1\,(+3.24), ROUGE-L\,(+2.25), and a decreased repetition-rate (-4.98\%).