Automatic evaluation of scientific abstracts through natural language processing
This work addresses the need for efficient ranking of methods in scientific literature, particularly for domain-specific applications like oil production, but it is incremental as it builds on existing NLP techniques.
The authors tackled the problem of automatically evaluating scientific abstracts by developing an NLP framework that classifies, segments, and ranks them based on sentiment analysis of results, achieving promising results in experiments with oil production anomaly abstracts.
This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.