Automated Scientific Reviewing

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Source arXiv

Computation and Language

Automated Scientific Reviewing

Can We Automate Scientific Reviewing?

The rapid development of science and technology has been accompanied by anexponential growth in peer-reviewed scientific publications.

Thus, providing high-quality reviews of this growing number of papers is a significant challenge.

We collect a dataset of papers in the machine learning domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers to generate reviews.

Comprehensive experimental results show that system-generated reviews tend to touch upon more aspects of the paper than human-written reviews, but the generated text can suffer from lower constructiveness for all aspects except the explanation of the core ideas of the papers, which are largely factually correct.

Authors

Weizhe Yuan, Pengfei Liu, Graham Neubig

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