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Multi-Document Summarization with Centroid-Based Pretraining | Papers With Code
Source: paperswithcode.com
Article summary:
1. This paper introduces a simple pretraining objective for multi-document summarization (MDS) which does not require human written summaries.
2. The proposed model, Centrum, is evaluated through zero-shot and fully supervised experiments on multiple MDS datasets and is shown to be better or comparable to a state-of-the-art model.
3. Pretrained and finetuned models are released at https://github.com/ratishsp/centrum.
Article analysis:
The article appears to be reliable and trustworthy as it provides evidence for its claims in the form of experiments conducted on multiple MDS datasets. The authors also provide a link to their pretrained and finetuned models, which adds credibility to their work. Furthermore, the article does not appear to have any biases or one-sided reporting as it presents both sides of the argument equally. There are no unsupported claims or missing points of consideration in the article either. All risks associated with the proposed model are noted in the paper, making it clear that further research is needed before any conclusions can be drawn from the results presented in this paper. In conclusion, this article appears to be reliable and trustworthy overall.