Leveraging peer-review aspects for extractive and abstractive summarization of scientific articles

This research introduces an innovative framework that supports the peer-review process by automatically extracting the following four key aspects of a scientific paper: contribution, motivation, claims, and claims support. Leveraging these extracted aspects, we generate extractive and abstractive summaries of scientific papers. Additionally, we provide a benchmarking corpus containing 1000 aspect-related sentences extracted from 40 scientific articles, which can serve as a valuable resource for evaluating various aspect extraction methods. Experimental findings reveal that our automated aspect extraction system successfully identifies between 86 and 92% of sentences related to each aspect with precision ranging from 84 to 94%. The aspect-based extractive summaries outperformed the original paper abstracts in terms of the Rouge scores as well as in Relevance, Consistency, Fluency, and Coherence dimensions. Furthermore, our study demonstrates that by prompting the LLMs using the paper itself along with the extracted aspects, the LLM-generated summaries exhibit superior performance compared to prompting with either the paper or the abstract only.