Automatic evaluation of scientific abstracts through natural language processing

TL;DR

This paper introduces a natural language processing framework that classifies, segments, and evaluates scientific abstracts to efficiently rank research methods based on their results, demonstrated on oil production anomaly abstracts.

Contribution

The paper presents a novel NLP-based framework for automatic classification, segmentation, and evaluation of scientific abstracts to facilitate quick ranking of research methods.

Findings

Effective classification of abstracts into problem, methodology, and results.

Successful ranking of abstracts based on sentiment analysis of results.

Promising validation on oil production anomaly abstracts.

Abstract

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.