Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization

Understand

Sentence summarization shortens given texts while maintaining core contents of the texts.

• Unsupervised approaches have been studied to summarize texts without human-written summaries.
• However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization.
• In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries.