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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 the unequal importance for sentences not in the pseudo extraction oracle.
3. Reinforcement learning is used to harmonize the optimization between training and testing, resulting in improved performance on Multi-News, Multi-XScience, and WikiCatSum corpora.
Article analysis:
The article is written by a team of researchers from various universities and research institutes, which adds credibility to its content. The article provides a detailed description of the proposed method and its advantages over existing methods, which makes it easy to understand for readers with some technical background. The authors also provide evidence for their claims through experiments on three different datasets, which further strengthens their argument.
However, there are some potential biases that should be noted. For example, the authors do not discuss any possible risks associated with their proposed method or any unexplored counterarguments that could be raised against it. Additionally, they do not present both sides equally when discussing existing methods; instead they focus more on highlighting the advantages of their own approach over others. Furthermore, there is no mention of any ethical considerations related to using pre-trained language models for summarization tasks.