Agent-based solution for improving abstracts

Abstract

Writing abstracts in a comprehensive and meaningful manner is a challenge for any researcher. However an abstract includes limited set of verbs and standard phrases and other good practices of structuring the contents. A research has been conducted to develop an Agent-based Solution for Improving Abstracts. This solution is based on multi agent systems technology and natural language processing together with commonly used verb phrases and other good practices. The system has been developed with nine agents, namely, coordination agent, parser agent, problem agent, solution agent, conclusion agent, content agent, synonym agent, improvement agent and restructure agent. The coordination agent coordinates entire process. The parser agent identifies syntactic information of each sentence and prepares the contents of the abstract for further analysis. The problem agent ensures whether the research problem has been stated in the early part of the abstract and it‘s proportion within the abstract. The solution agent checks for the contents in terms of concepts such as hypothesis, methodology, approach, design, implementation, methods, theoretical framework, technology, hardware, software, and sampling based on the key words. The conclusion agent searches for concepts such as testing, evaluation, data analysis and statistical significance based on the key words. The content agent, improvement agent, synonym agent, and restructure agent are responsible to offer guidelines to modify and improving of the abstract. More importantly, these agents interact with each other and deliberate to reach consensus regarding a solution. For instance, problem agent and solution agent may agree on the proportion of respective contents within the abstract. Each agent has its own Ontology for deliberating with other agents. The Stanford CoreNLP Natural Language Processing Toolkit has been used to develop parser and JADE has been used for development of the entire multi agent system. The system has been developed with JAVA to run on Windows. It has been incrementally tested, and shown interesting results related to checking for completeness of the abstract in terms required materials and suggestion for improvements.