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Article summary:
1. This paper presents an extract-then-abstract Transformer framework to address the challenge of extremely long input in Multi-Document Summarization (MDS).
2. A loss weighting mechanism is proposed to make the model aware of unequal importance for sentences not in the pseudo extraction oracle.
3. Reinforcement learning is used to harmonize optimization between training and testing, and experiments show that this framework outperforms strong baselines on Multi-News, Multi-XScience, and WikiCatSum corpora.
Article analysis:
The article provides a detailed overview of a new approach to multi-document summarization which uses pre-trained language models to construct a hierarchical extractor for salient sentence selection across documents and an abstractor for rewriting the selected contents as summaries. The article also proposes a loss weighting mechanism and reinforcement learning method to improve the performance of the model. The article appears to be well researched and reliable, with evidence provided from experiments conducted on three different corpora. There does not appear to be any bias or one-sided reporting in the article, nor any unsupported claims or missing points of consideration. All claims are supported by evidence from experiments, and all counterarguments are explored. There is no promotional content or partiality present in the article either, and possible risks are noted where appropriate. The article presents both sides equally, providing an unbiased overview of the research presented.