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