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A lot of manual work goes into identifying a topic for an article. With alarge volume of articles, the manual process can be exhausting. Our approachaims to address this issue by automatically extracting topics from the text oflarge Numbers of articles. This approach takes into account the efficiency ofthe process. Based on existing N-gram analysis, our research examines how oftencertain words appear in documents in order to support automatic topicextraction. In order to improve efficiency, we apply custom filtering standardsto our research. Additionally, delete as many noncritical or irrelevant phrasesas possible. In this way, we can ensure we are selecting unique keyphrases foreach article, which capture its core idea. For our research, we chose to centeron the autonomous vehicle domain, since the research is relevant to our dailylives. We have to convert the PDF versions of most of the research papers intoeditable types of files such as TXT. This is because most of the researchpapers are only in PDF format. To test our proposed idea of automating,numerous articles on robotics have been selected. Next, we evaluate ourapproach by comparing the result with that obtained manually.

An N-gram based approach to auto-extracting topics from research articles