Efficient and Interpretable Compressive Text Summarisation with
  Unsupervised Dual-Agent Reinforcement Learning
Peggy Tang Junbin Gao Lei Zhang Zhiyong Wang
Recently, compressive text summarisation offers a balance between the
conciseness issue of extractive summarisation and the factual hallucination
issue of abstractive summarisation. However, most existing compressive
summarisation methods are supervised, relying on the expensive effort of
creating a new training dataset with corresponding compressive summaries. In
this paper, we propose an efficient and interpretable compressive summarisation
method that utilises unsupervised dual-agent reinforcement learning to optimise
a summary's semantic coverage and fluency by simulating human judgment on
summarisation quality. Our model consists of an extractor agent and a
compressor agent, and both agents have a multi-head attentional pointer-based
structure. The extractor agent first chooses salient sentences from a document,
and then the compressor agent compresses these extracted sentences by selecting
salient words to form a summary without using reference summaries to compute
the summary reward. To our best knowledge, this is the first work on
unsupervised compressive summarisation. Experimental results on three widely
used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves
promising performance and a significant improvement on Newsroom in terms of the
ROUGE metric, as well as interpretability of semantic coverage of summarisation
results.